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13. The system of claim 11 , wherein the text characteristic is a font size, and wherein the changing of the characteristic includes changing the font size from a first size to a second size that is different than the first size.

13. The system of claim 11 , wherein the text characteristic is a font size, and wherein the changing of the characteristic includes changing the font size from a first size to a second size that is different than the first size. 14. The system of claim 13 , wherein the difference between the first and second sizes is proportional to the amount of adjustment of the adjustable slide control.

12. The method of claim 11 , wherein performing the non-concurrency analysis further comprises: analyzing the control flow graph and the region tree to identify plural parallel regions; analyzing the parallel region associated with the variable to identify plural static phases, each static phases having one or more nodes; and comparing nodes of the control flow graph associated with the variable and nodes of the static phases to determine non-concurrency at compile time for nodes in the same phase.

12. The method of claim 11 , wherein performing the non-concurrency analysis further comprises: analyzing the control flow graph and the region tree to identify plural parallel regions; analyzing the parallel region associated with the variable to identify plural static phases, each static phases having one or more nodes; and comparing nodes of the control flow graph associated with the variable and nodes of the static phases to determine non-concurrency at compile time for nodes in the same phase. 19. The method of claim 12 wherein comparing nodes associated with the variable and static phases further comprises: determining that two nodes are in the same phase and same single construct; and determining non-concurrency for the nodes if none barrier free path exists from the header of the immediately enclosing loop to the node directive.

4. The speech recognition system of claim 3 , further comprising an initializer component that can initialize a convolutive distortion mean vector by setting each element of the convolutive distortion mean vector to zero, wherein the updater component updates the parameters of the second model based at least in part upon the convolutive distortion mean vector.

4. The speech recognition system of claim 3 , further comprising an initializer component that can initialize a convolutive distortion mean vector by setting each element of the convolutive distortion mean vector to zero, wherein the updater component updates the parameters of the second model based at least in part upon the convolutive distortion mean vector. 5. The speech recognition system of claim 4 , wherein the initializer component initializes an additive distortion mean vector using sample estimates from at least a first plurality of frames from the received distorted speech utterance, wherein the updater component updates the parameters of the second model based at least in part upon the additive distortion mean vector.

7. One or more non-transitory computer-readable storage media having instructions stored thereon that, responsive to execution by one or more processors, causes the one or more processors to perform operations comprising: receiving an indication indicating selection of a character, the selection of the character through a gesture-sensitive character-entry interface; presenting or causing presentation of, responsive to the selection of the character, based on the character, and through a completion interface, multiple characters adjacent a location at which the character was selected or superimposed over at least a portion of the gesture-sensitive character-entry interface, at least one of the multiple characters being a multi-string having or completing a long word and a short word, the short word being shorter than the long word and being a constituent part of the long word, the short word and the long word being presented at a same time and in the completion interface; enabling selection, through the completion interface, to select the short word or the long word, wherein selecting the short word would be selecting a portion of the multiple characters for the long word; receiving selection, through the completion interface, of the short word or the long word; and providing or presenting the selected short word or long word.

7. One or more non-transitory computer-readable storage media having instructions stored thereon that, responsive to execution by one or more processors, causes the one or more processors to perform operations comprising: receiving an indication indicating selection of a character, the selection of the character through a gesture-sensitive character-entry interface; presenting or causing presentation of, responsive to the selection of the character, based on the character, and through a completion interface, multiple characters adjacent a location at which the character was selected or superimposed over at least a portion of the gesture-sensitive character-entry interface, at least one of the multiple characters being a multi-string having or completing a long word and a short word, the short word being shorter than the long word and being a constituent part of the long word, the short word and the long word being presented at a same time and in the completion interface; enabling selection, through the completion interface, to select the short word or the long word, wherein selecting the short word would be selecting a portion of the multiple characters for the long word; receiving selection, through the completion interface, of the short word or the long word; and providing or presenting the selected short word or long word. 8. The media of claim 7 , wherein presenting or causing presentation is responsive to determining that a gesture selecting the character through the gesture-sensitive character-entry interface has not ended or indicates selection of the completion interface.

1. An apparatus comprising: a plurality of input keys collectively forming an alphabetic keypad layout and an alphanumeric keypad layout, wherein the plurality of input keys includes a first input key associated with an alphabetic character set of the alphabetic keypad layout and further associated with an alphanumeric character set of the alphanumeric keypad layout, the alphabetic character set including a first set of alphabetic characters and the alphanumeric character set including a number and a second set of alphabetic characters; wherein the apparatus is configured to operate in a first mode to receive as input a first alphabetic character out of said first set of alphabetic characters in response to an activation of the first input key, and in a second mode to receive as input a second alphabetic character out of the second set of alphabetic characters in response to an activation of the first input key, the second alphabetic character out of the second set of alphabetic characters being separate and distinct from the first alphabetic character out of the first set of alphabetic characters; and wherein the alphabetic keypad layout includes two or more alphabetic characters sequentially reordered to reduce a number of words generated by a predictive input routine; and wherein: the apparatus is configured, as part of receiving as input the first alphabetic character out of the first set of alphabetic characters in the first mode, to receive a single activation of the first input key; and the apparatus is further configured, as part of receiving as input the second alphabetic character out of the second set of alphabetic characters in the second mode, to allow for selection of the second alphabetic character out of the second set of alphabetic characters through one or more consecutive activations of the first input key.

1. An apparatus comprising: a plurality of input keys collectively forming an alphabetic keypad layout and an alphanumeric keypad layout, wherein the plurality of input keys includes a first input key associated with an alphabetic character set of the alphabetic keypad layout and further associated with an alphanumeric character set of the alphanumeric keypad layout, the alphabetic character set including a first set of alphabetic characters and the alphanumeric character set including a number and a second set of alphabetic characters; wherein the apparatus is configured to operate in a first mode to receive as input a first alphabetic character out of said first set of alphabetic characters in response to an activation of the first input key, and in a second mode to receive as input a second alphabetic character out of the second set of alphabetic characters in response to an activation of the first input key, the second alphabetic character out of the second set of alphabetic characters being separate and distinct from the first alphabetic character out of the first set of alphabetic characters; and wherein the alphabetic keypad layout includes two or more alphabetic characters sequentially reordered to reduce a number of words generated by a predictive input routine; and wherein: the apparatus is configured, as part of receiving as input the first alphabetic character out of the first set of alphabetic characters in the first mode, to receive a single activation of the first input key; and the apparatus is further configured, as part of receiving as input the second alphabetic character out of the second set of alphabetic characters in the second mode, to allow for selection of the second alphabetic character out of the second set of alphabetic characters through one or more consecutive activations of the first input key. 22. The apparatus of claim 1 , wherein the two or more alphabetic characters are sequentially reordered in comparison to a sequence of the corresponding two or more alphabetic characters of a QWERTY, QWERTZ, DVORAK, or telephone keypad layout.

17. An apparatus for retrieving relevant documents from a corpus of documents based on a search query, the apparatus comprising: storage means for storing the corpus of documents; input means for inputting the corpus of documents and the search query; a controller for retrieving relevant documents from the corpus of documents, the controller comprising: index term signature generating means for generating an index term signature for each index term in the corpus of documents, the index term signature being based on a hash function of a predetermined number of adjacent terms adjacent to the index term; list generating means for generating a list containing the index terms in the corpus of documents, the list associating an index term with a document identifier and corresponding index term signatures occurring in the document; query signature generating means for generating a query signature for the search query excluding a reference term, the query signature being based on the hash function of the adjacent query terms adjacent to the reference term; and comparing means for comparing the query signature to the index term signatures in the list to identify index term signatures that match the query signature of the reference term, the reference term of the query signature being equivalent to the index term of the list; and output means for outputting a document list indicating the documents that contain the identified index term signatures.

17. An apparatus for retrieving relevant documents from a corpus of documents based on a search query, the apparatus comprising: storage means for storing the corpus of documents; input means for inputting the corpus of documents and the search query; a controller for retrieving relevant documents from the corpus of documents, the controller comprising: index term signature generating means for generating an index term signature for each index term in the corpus of documents, the index term signature being based on a hash function of a predetermined number of adjacent terms adjacent to the index term; list generating means for generating a list containing the index terms in the corpus of documents, the list associating an index term with a document identifier and corresponding index term signatures occurring in the document; query signature generating means for generating a query signature for the search query excluding a reference term, the query signature being based on the hash function of the adjacent query terms adjacent to the reference term; and comparing means for comparing the query signature to the index term signatures in the list to identify index term signatures that match the query signature of the reference term, the reference term of the query signature being equivalent to the index term of the list; and output means for outputting a document list indicating the documents that contain the identified index term signatures. 20. The apparatus of claim 17, wherein the input means is one of a keyboard, a touchscreen, an image scanner and a computer terminal.

1. A method of using a local communication network to generate a speech recognition model, the method comprising: retrieving for an individual a list of numbers in a calling history; identifying a local neighborhood associated with each number in the calling history; truncating the local neighborhood associated with each number based on at least one parameter; retrieving a local communication network associated with each number in the calling history and each phone number associated with a local neighborhood; and creating, via a processor, a language model for the individual based on the retrieved local communication network.

1. A method of using a local communication network to generate a speech recognition model, the method comprising: retrieving for an individual a list of numbers in a calling history; identifying a local neighborhood associated with each number in the calling history; truncating the local neighborhood associated with each number based on at least one parameter; retrieving a local communication network associated with each number in the calling history and each phone number associated with a local neighborhood; and creating, via a processor, a language model for the individual based on the retrieved local communication network. 3. The method of claim 1 , further comprising implementing the created language model in a spoken dialog system.

1. A user's intention deduction apparatus, comprising: a detector configured to generate motion information based upon detected motion of a user; a first predictor configured to predict a part of the user's intention using the motion information; and a second predictor configured to predict the user's intention using the part of the user's intention predicted by the first predictor and multimodal information received from at least one multimodal sensor, wherein the first predictor predicts the part of the user's intention to be the selection of an object displayed by a screen based upon an angle and a distance between the user's two hands.

1. A user's intention deduction apparatus, comprising: a detector configured to generate motion information based upon detected motion of a user; a first predictor configured to predict a part of the user's intention using the motion information; and a second predictor configured to predict the user's intention using the part of the user's intention predicted by the first predictor and multimodal information received from at least one multimodal sensor, wherein the first predictor predicts the part of the user's intention to be the selection of an object displayed by a screen based upon an angle and a distance between the user's two hands. 10. The user's intention deduction apparatus of claim 1 , wherein, in response to the predicted part of the user's intention being to select an object displayed on a display screen, the second predictor is further configured to predict the user's intention as at least one of deletion, classification, and arrangement of the selected object using the multimodal information.

13. The method of claim 1 , where the query context includes information relating to phrases and/or pairs of words that occur within predetermined distances of one another in the plurality of documents.

13. The method of claim 1 , where the query context includes information relating to phrases and/or pairs of words that occur within predetermined distances of one another in the plurality of documents. 14. The method of claim 13 , where comparing the query context to the alternative terms includes checking that an alternative term occurs above a predetermined threshold frequency.

13. A non-transitory computer-readable medium including instructions stored therein that, when executed by at least one computing device, cause the at least one computing device to: receive a request for mapping information to be displayed to a user; determine point of interest (POI) data associated with the mapping information, the POI data including one or more POIs; analyze the request to identify a specified area of interest; determine a value of a distance function based at least in part on the specified area of interest; determine a relevance score of each POI in the POI data based at least in part on the distance function applied as a multiplier to a baseline score of the each POI within the specified area of interest; select a portion of the POI data based at least in part upon the relevance score of the each POI of the POI data; and provide the portion of the POI data for display with the mapping information.

13. A non-transitory computer-readable medium including instructions stored therein that, when executed by at least one computing device, cause the at least one computing device to: receive a request for mapping information to be displayed to a user; determine point of interest (POI) data associated with the mapping information, the POI data including one or more POIs; analyze the request to identify a specified area of interest; determine a value of a distance function based at least in part on the specified area of interest; determine a relevance score of each POI in the POI data based at least in part on the distance function applied as a multiplier to a baseline score of the each POI within the specified area of interest; select a portion of the POI data based at least in part upon the relevance score of the each POI of the POI data; and provide the portion of the POI data for display with the mapping information. 14. The non-transitory computer-readable medium of claim 13 , wherein the request includes one or more parameters including at least one of at least one query keyword, a query constraint, the specified area of interest, or a location of a user.

1. A method of playing a game for the selection and juxtaposition of optically discernible symbols to form intelligible groupings comprising a plurality of playing pieces, each having at least two flat faces, at least some of which faces bear at least one optically discernible symbol, the pieces being adapted for resting on a flat, substantially horizontal surface in a position such that one flat symbol-bearing face is in a generally upward-facing position, a game support, a playing-piece receptacle rotatably mounted on said support and having a substantially flat receiving section whereon said playing pieces may be positioned when the receptacle is in a first extreme position so that each piece has an initial face in the upward position, said receptacle being rotatable to a second extreme position, and actuating means operatively attached to said support for scrambling said playing pieces after they have been positioned on the receiving section of the receptacle so that at least some of the pieces have a face in an upward position that is different from said initial face, said actuating means being cocked by movement of said receptacle to said first extreme position and scrambling said pieces as it snaps the receptacle to said second extreme position, said method comprising the steps of: (a) a first player selecting a plurality of the playing pieces, placing said playing pieces into the playing piece receptacle, said receptacle being in the first extreme position, and positioning said pieces on the receiving section of said receptacle so that each of said pieces has an initial face in the upward position; (b) said first player utilizing the actuating means to scramble said playing pieces so that at least some of said pieces have a face in the upward position that is different from said initial face; (c) said first player, within a first predetermined period of time from the completion of the scrambling of the playing pieces effected by utilization of said actuating means, selecting a predetermined number of pieces and juxtaposing them so that at least one intelligible grouping of the symbols appearing on the faces of the pieces in the upward position is formed; (d) said first player and one or more other players, within a second predetermined period of time from the formation of said intelligible groupings of symbols by said first player, forming additional intelligible groupings of symbols utilizing exclusively some or all of the symbols which comprise the intelligible groupings of symbols formed by said first player; and (e) assigning values to the intelligible groupings of symbols formed by said first player and by said other players respectively whereby it may be determined which player has accumulated the highest total value.

1. A method of playing a game for the selection and juxtaposition of optically discernible symbols to form intelligible groupings comprising a plurality of playing pieces, each having at least two flat faces, at least some of which faces bear at least one optically discernible symbol, the pieces being adapted for resting on a flat, substantially horizontal surface in a position such that one flat symbol-bearing face is in a generally upward-facing position, a game support, a playing-piece receptacle rotatably mounted on said support and having a substantially flat receiving section whereon said playing pieces may be positioned when the receptacle is in a first extreme position so that each piece has an initial face in the upward position, said receptacle being rotatable to a second extreme position, and actuating means operatively attached to said support for scrambling said playing pieces after they have been positioned on the receiving section of the receptacle so that at least some of the pieces have a face in an upward position that is different from said initial face, said actuating means being cocked by movement of said receptacle to said first extreme position and scrambling said pieces as it snaps the receptacle to said second extreme position, said method comprising the steps of: (a) a first player selecting a plurality of the playing pieces, placing said playing pieces into the playing piece receptacle, said receptacle being in the first extreme position, and positioning said pieces on the receiving section of said receptacle so that each of said pieces has an initial face in the upward position; (b) said first player utilizing the actuating means to scramble said playing pieces so that at least some of said pieces have a face in the upward position that is different from said initial face; (c) said first player, within a first predetermined period of time from the completion of the scrambling of the playing pieces effected by utilization of said actuating means, selecting a predetermined number of pieces and juxtaposing them so that at least one intelligible grouping of the symbols appearing on the faces of the pieces in the upward position is formed; (d) said first player and one or more other players, within a second predetermined period of time from the formation of said intelligible groupings of symbols by said first player, forming additional intelligible groupings of symbols utilizing exclusively some or all of the symbols which comprise the intelligible groupings of symbols formed by said first player; and (e) assigning values to the intelligible groupings of symbols formed by said first player and by said other players respectively whereby it may be determined which player has accumulated the highest total value. 2. A method according to claim 1 wherein said first predetermined time period is less than said second predetermined time period.

37. The machine system of claim 26 wherein the automated user activities monitor is further configured to automatically detect and record indications of intensity or rapidity of emotional involvement concurrently associated with the machine usage activities of the first user.

37. The machine system of claim 26 wherein the automated user activities monitor is further configured to automatically detect and record indications of intensity or rapidity of emotional involvement concurrently associated with the machine usage activities of the first user. 38. The machine system of claim 37 wherein the automatically detected and recorded indications of intensity or rapidity of emotional involvement include at least one of: eye blinking activities of the first user; heart rate of the first user; breathing rate of the first user; depth of breathing of the first user; fidgeting by the first user; posture of user's body; facial grimaces of the first user; galvanic skin response of the first user; hand gesturing by the first user; blood pressure of the first user; blushing or paleness of the first user; skin temperature of the first user; muscle tensing by the first user; imagery in an infra-red (IR) range of the first user; and change of distance between user and display.

11. Apparatus as in claim 1 further comprising: animation means coupled to the integrator means, to the sound emitting means and to the display means, said animation means responsive to said signals for generating an encoded model of the animated object, the display means responsive to said encoded model to display the visual images of the animated objects.

11. Apparatus as in claim 1 further comprising: animation means coupled to the integrator means, to the sound emitting means and to the display means, said animation means responsive to said signals for generating an encoded model of the animated object, the display means responsive to said encoded model to display the visual images of the animated objects. 12. Apparatus as in claim 11 wherein said animation means comprises: first animation means responsive to said signals for generating a first digitized encoded model defining the characteristics of anthropomorphic objects; and second animation means responsive to said signals for generating a second digitized encoded model defining the characteristics of physical objects.

12. A method for scanning an image comprising: sending, from a source manager operating in accordance with the TWAIN protocol to an acquisition layer, a request for an image, the request originating from an application; scanning the image with a scanner in response to the request for the image being sent to the acquisition layer from the source manager; performing by the acquisition layer optical character recognition on the image to obtain machine editable text, wherein the optical character recognition on the image is performed independent of the image as received by the application; forwarding the image from the acquisition layer to the application; and, forwarding the machine editable text from the acquisition layer to a location accessible by a user of the application.

12. A method for scanning an image comprising: sending, from a source manager operating in accordance with the TWAIN protocol to an acquisition layer, a request for an image, the request originating from an application; scanning the image with a scanner in response to the request for the image being sent to the acquisition layer from the source manager; performing by the acquisition layer optical character recognition on the image to obtain machine editable text, wherein the optical character recognition on the image is performed independent of the image as received by the application; forwarding the image from the acquisition layer to the application; and, forwarding the machine editable text from the acquisition layer to a location accessible by a user of the application. 13. The method of claim 12 wherein forwarding the machine editable text from the acquisition layer to a location includes at least one of the following: placing the machine editable text in a system clipboard; and placing the machine editable text in a disk file.

35. A method of encoding of syntactic elements of a data stream, wherein: before beginning of encoding of the data stream, cells of all context models are initialized with predefined values, so that each context model contains in each cell thereof data on a probability and a counter of a context occurrence number, a number of the cells stored in each context model is selected to be not less than a number of all possible states of context elements associated with a respective context model, and in the process of encoding of at least a portion of bits of the data stream: a group of context models is selected that comprises at least two context models of different size; values of at least two context elements associated the selected group of context models are calculated; selection of the cells in context models is carried out using values of the context elements associated with the respective context model; one context model is selected from the group of context models using values of individual counters of a context occurrence number stored in the selected cells; the data on the probability is extracted from the selected cell of the selected context model, which data is used for entropy encoding of a current bit of the data stream, and/or for selecting a mode of writing encoded bits into the data stream directly; data in the selected cell of the selected context model as well as in all the context models of the group of context models, which have a size greater than that of the selected context model, is updated; and a procedure of probability inheritance from the selected context model is carried out in respect of the selected cells of those context models of the group of context models for which predefined inheritance criteria are met.

35. A method of encoding of syntactic elements of a data stream, wherein: before beginning of encoding of the data stream, cells of all context models are initialized with predefined values, so that each context model contains in each cell thereof data on a probability and a counter of a context occurrence number, a number of the cells stored in each context model is selected to be not less than a number of all possible states of context elements associated with a respective context model, and in the process of encoding of at least a portion of bits of the data stream: a group of context models is selected that comprises at least two context models of different size; values of at least two context elements associated the selected group of context models are calculated; selection of the cells in context models is carried out using values of the context elements associated with the respective context model; one context model is selected from the group of context models using values of individual counters of a context occurrence number stored in the selected cells; the data on the probability is extracted from the selected cell of the selected context model, which data is used for entropy encoding of a current bit of the data stream, and/or for selecting a mode of writing encoded bits into the data stream directly; data in the selected cell of the selected context model as well as in all the context models of the group of context models, which have a size greater than that of the selected context model, is updated; and a procedure of probability inheritance from the selected context model is carried out in respect of the selected cells of those context models of the group of context models for which predefined inheritance criteria are met. 64. The method of encoding according to claim 35 , wherein at least one syntactic element, depending on the value of at least one context element associated therewith, is encoded into the data stream by using a predefined fixed value of the probability.

6. A non-transitory computer storage medium encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving a first executable statement, the first executable statement referencing a second set of fields in a table in a relational database, the first executable statement having instructions to cause the database to perform operations on data in the table; generating a second executable statement based on the first set of fields and the first executable statement, comprising: determining a mapping between the first set of fields and the second set of fields, comprising: identifying a first data type of a first field in the first set of fields, identifying a second data type of a second field in the second set of fields, identifying a conversion command to convert from the first data type to the second data type, and adding the conversion command to the second executable statement, specifying a derived table using the corresponding values and the mapping, and generating instructions to cause the database to perform the operations on the derived table; and sending the second executable statement to the database.

6. A non-transitory computer storage medium encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving a first executable statement, the first executable statement referencing a second set of fields in a table in a relational database, the first executable statement having instructions to cause the database to perform operations on data in the table; generating a second executable statement based on the first set of fields and the first executable statement, comprising: determining a mapping between the first set of fields and the second set of fields, comprising: identifying a first data type of a first field in the first set of fields, identifying a second data type of a second field in the second set of fields, identifying a conversion command to convert from the first data type to the second data type, and adding the conversion command to the second executable statement, specifying a derived table using the corresponding values and the mapping, and generating instructions to cause the database to perform the operations on the derived table; and sending the second executable statement to the database. 10. The non-transitory computer storage medium of claim 6 , wherein generating a second executable statement based on the first set of fields and the first executable statement further comprises: providing an alias for the derived table; and updating the second executable statement to reference the alias.

8. A system for personalized medical content recommendation, comprising: a processor adapted to access, from a data storage medium, a patient's medical profile, a concept or relationship relating to the patient's medical profile, and medical content relevant to the concept or relationship, wherein the concept or relationship is determined worthy of attention and possible reporting to the patient due to recognizing a level of risk to the patient based on an analysis of the patient's medical profile, the concept or relationship and the relevant medical content; a processor adapted to determine a score for the concept or relationship to the patient's medical profile; a processor adapted to enhance the score using a medical context score wherein the enhanced medical context score is determined based on a proximity measure between the concept or relationship and potential intersections formed by the concept or relationship; a processor adapted to enhance the score with an additional knowledge score wherein the enhanced additional knowledge score is determined based on one or more risk levels associated with the concept or relationship and additional medical knowledge that is obtained by searching external sources for medical content relevant to the concept or relationship; and a processor adapted to recommend medical content with respect to a patient medical profile based on the enhanced medical context and additional knowledge scores, wherein the medical content is sorted in a first order according to level of risk to the patient.

8. A system for personalized medical content recommendation, comprising: a processor adapted to access, from a data storage medium, a patient's medical profile, a concept or relationship relating to the patient's medical profile, and medical content relevant to the concept or relationship, wherein the concept or relationship is determined worthy of attention and possible reporting to the patient due to recognizing a level of risk to the patient based on an analysis of the patient's medical profile, the concept or relationship and the relevant medical content; a processor adapted to determine a score for the concept or relationship to the patient's medical profile; a processor adapted to enhance the score using a medical context score wherein the enhanced medical context score is determined based on a proximity measure between the concept or relationship and potential intersections formed by the concept or relationship; a processor adapted to enhance the score with an additional knowledge score wherein the enhanced additional knowledge score is determined based on one or more risk levels associated with the concept or relationship and additional medical knowledge that is obtained by searching external sources for medical content relevant to the concept or relationship; and a processor adapted to recommend medical content with respect to a patient medical profile based on the enhanced medical context and additional knowledge scores, wherein the medical content is sorted in a first order according to level of risk to the patient. 12. The system as claimed in claim 8 , including: an explanation component for providing an explanation of a medical content recommendation based on a matching concept or relationship.

9. Non-transient storage media that stores software which when run on a computer performs a computer implemented parsing method, comprising: representing a grammar of a first programming language in member fields and data types of object-oriented classes of a second programming language as an empty program semantic tree; and, building a new program semantic tree that represents source code written in the first programming language, the new program semantic tree being built by a reflection technique in which the member fields and data types of the object-oriented classes of the second programming language as set out in the empty program semantic tree are modified during the building of the new program semantic tree, wherein building the new program semantic tree includes utilizing one or more of: a top level parsing routine to call token specific parsers, a parser to handle tokens in a token sequence, and/or a precedence chooser parser to parse programming syntax involving mathematical operators.

9. Non-transient storage media that stores software which when run on a computer performs a computer implemented parsing method, comprising: representing a grammar of a first programming language in member fields and data types of object-oriented classes of a second programming language as an empty program semantic tree; and, building a new program semantic tree that represents source code written in the first programming language, the new program semantic tree being built by a reflection technique in which the member fields and data types of the object-oriented classes of the second programming language as set out in the empty program semantic tree are modified during the building of the new program semantic tree, wherein building the new program semantic tree includes utilizing one or more of: a top level parsing routine to call token specific parsers, a parser to handle tokens in a token sequence, and/or a precedence chooser parser to parse programming syntax involving mathematical operators. 10. Non-transient storage media stores software as in claim 9 wherein building the new program semantic tree includes: converting the source code into abstract tokens, the abstract tokens including: token sequences; token choosers; precedence tokens; unparsed tokens; token lists; and terminal tokens.

19. The short film generation/reproduction apparatus according to claim 1 , further comprising: a short film selection unit operable to select the video to be reproduced; and a short film reproduction unit operable to read out, from the database unit, the scenario of the video selected by the short film selection unit, and one still picture and the music defined in the scenario, and to reproduce the video based on the scenario.

19. The short film generation/reproduction apparatus according to claim 1 , further comprising: a short film selection unit operable to select the video to be reproduced; and a short film reproduction unit operable to read out, from the database unit, the scenario of the video selected by the short film selection unit, and one still picture and the music defined in the scenario, and to reproduce the video based on the scenario. 24. The short film generation/reproduction apparatus according to claim 19 , further comprising an operation unit operable to operate the short film generation/reproduction apparatus and a display device for displaying the video, the display device being connected to the short film generation/reproduction apparatus, wherein the short film reproduction unit modulates a signal obtained by reproducing the video into a video signal so as to output the video signal, and starts reproducing the video when a predetermined button is pressed down, the button being included in the operation unit and being assigned the video signal.

23. The computer system of claim 19 , wherein the instructions for determining the popularity metric of a candidate search result further include: instructions for determining a model predicting a likelihood of a user selection of a search result using multiple users' search histories; and instructions for applying the model to the user's search history to generate the popularity metric of the candidate search result.

23. The computer system of claim 19 , wherein the instructions for determining the popularity metric of a candidate search result further include: instructions for determining a model predicting a likelihood of a user selection of a search result using multiple users' search histories; and instructions for applying the model to the user's search history to generate the popularity metric of the candidate search result. 24. The computer system of claim 23 , wherein the instructions for determining the model further include: instructions for generating a profile that characterizes the multiple users' behaviors over multiple search results in the multiple users' search histories; and instructions for determining as the model a set of coefficients from the profile, wherein the set of coefficients, when applied to a set of search results associated from a specific user's search history, is configured to predict the likelihood of the specific user's selection of each of the search results in the future.

13. The system of claim 11 , including rules that are configured to determine whether a particular organization is in conformance to various conformance criteria included in said matrix.

13. The system of claim 11 , including rules that are configured to determine whether a particular organization is in conformance to various conformance criteria included in said matrix. 14. The system of claim 13 , including a communication device that allows monitoring services to independently access the system to determine whether said schools or agencies are in conformance with said matrix rules.

2. The computer implemented method of claim 1 , wherein the computer is a server is a cloud computing environment.

2. The computer implemented method of claim 1 , wherein the computer is a server is a cloud computing environment. 4. The computer implemented method of claim 2 , wherein the name field indicates the value field indicates an author of the document and the value field indicates the author.

15. An apparatus comprising: an input/output interface configured to receive an audio stream; a processor coupled to the input/output interface and configured to: segment the audio stream into a plurality of time segments using speaker segmentation and recognition (SSR), each of the plurality of time segments corresponding to a name label, to produce an SSR transcript; transcribe the audio stream into a plurality of word regions using automatic speech recognition (ASR), each of the plurality of word regions having an associated accuracy confidence, to produce an ASR transcript; identify a plurality of low confidence regions from the plurality of word regions, each of the low confidence regions having an associated accuracy confidence below a threshold; identify at least one likely name region from the ASR transcript using named entity recognition (NER) rules, wherein the NER rules analyze word regions to identify the at least one likely name region, and the NER rules associate each of the at least one likely name regions with a name label from the SSR transcript corresponding to one of a current, previous, or next time segment; filter the at least one likely name region with the plurality of low confidence regions to determine at least one low confidence name region; select all of the likely low confidence name regions associated with a selected name label, the selected name label being selected from the name labels in the SSR transcript; create a phoneme transcript from the audio stream for each of the selected likely name regions using a phoneme decoder; and correlate the selected name label with all of the phoneme transcripts for the selected likely name regions.

15. An apparatus comprising: an input/output interface configured to receive an audio stream; a processor coupled to the input/output interface and configured to: segment the audio stream into a plurality of time segments using speaker segmentation and recognition (SSR), each of the plurality of time segments corresponding to a name label, to produce an SSR transcript; transcribe the audio stream into a plurality of word regions using automatic speech recognition (ASR), each of the plurality of word regions having an associated accuracy confidence, to produce an ASR transcript; identify a plurality of low confidence regions from the plurality of word regions, each of the low confidence regions having an associated accuracy confidence below a threshold; identify at least one likely name region from the ASR transcript using named entity recognition (NER) rules, wherein the NER rules analyze word regions to identify the at least one likely name region, and the NER rules associate each of the at least one likely name regions with a name label from the SSR transcript corresponding to one of a current, previous, or next time segment; filter the at least one likely name region with the plurality of low confidence regions to determine at least one low confidence name region; select all of the likely low confidence name regions associated with a selected name label, the selected name label being selected from the name labels in the SSR transcript; create a phoneme transcript from the audio stream for each of the selected likely name regions using a phoneme decoder; and correlate the selected name label with all of the phoneme transcripts for the selected likely name regions. 20. The apparatus of claim 15 , wherein the automatic speech recognition (ASR) transcript comprises a plurality of time stamps associated with the plurality of word regions.

1. A method implemented by an apparatus for supplying electronic documents based on XML, the method comprising: supplying from a provider of a program broadcasting system to a client in the program providing system an electronic document that uses XML to describe programming information about a plurality of television broadcast programs scheduled for broadcast in the program broadcasting system, the electronic document having a hierarchical structure based on a prescribed syntax, the hierarchical structure including an upper fragment and a plurality of lower fragments located below the upper fragment in the hierarchical structure to describe, for each of the scheduled television broadcast programs, a program identifier, a title, broadcast information and corresponding program content information; and supplying from the provider to the client an update document to update the previously supplied electronic document, the update document having a structure based on the prescribed syntax and including the upper fragment and an invalid element to identify an invalid fragment as one of the lower fragments in the hierarchical structure, wherein the invalid fragment is related to one of the television broadcast programs described in the previously supplied electronic document, and wherein the update document indicates deletion of said invalid fragment from the electronic document according to the invalid element.

1. A method implemented by an apparatus for supplying electronic documents based on XML, the method comprising: supplying from a provider of a program broadcasting system to a client in the program providing system an electronic document that uses XML to describe programming information about a plurality of television broadcast programs scheduled for broadcast in the program broadcasting system, the electronic document having a hierarchical structure based on a prescribed syntax, the hierarchical structure including an upper fragment and a plurality of lower fragments located below the upper fragment in the hierarchical structure to describe, for each of the scheduled television broadcast programs, a program identifier, a title, broadcast information and corresponding program content information; and supplying from the provider to the client an update document to update the previously supplied electronic document, the update document having a structure based on the prescribed syntax and including the upper fragment and an invalid element to identify an invalid fragment as one of the lower fragments in the hierarchical structure, wherein the invalid fragment is related to one of the television broadcast programs described in the previously supplied electronic document, and wherein the update document indicates deletion of said invalid fragment from the electronic document according to the invalid element. 55. The method of claim 1 , wherein the invalid element includes a fragment identifier to identify the invalid fragment.

1. A talking electronic apparatus comprising: memory means for storing digital speech data and digital control data from which a plurality of requests in synthesized human speech for respective operator responses and appropriate operator responses corresponding to said plurality of requests may be respectively derived, speech synthesizer means operably associated with said memory means for converting said digital speech data into audible human speech, means for randomly accessing a portion of said digital speech data stored in said memory means from which a request for an operator response may be derived, means for transferring said randomly accessed portion of said digital speech data from said memory means to said speech synthesizer means to produce a randomly selected audible request in human speech, operator input means for receiving an operator response to said randomly selected audible request, and means responsive to said digital control data and said operator response to said randomly selected audible request for responding in a manner producing an output indicative of the appropriateness of said operator response with respect to the appropriate operator response corresponding to said randomly selected audible request.

1. A talking electronic apparatus comprising: memory means for storing digital speech data and digital control data from which a plurality of requests in synthesized human speech for respective operator responses and appropriate operator responses corresponding to said plurality of requests may be respectively derived, speech synthesizer means operably associated with said memory means for converting said digital speech data into audible human speech, means for randomly accessing a portion of said digital speech data stored in said memory means from which a request for an operator response may be derived, means for transferring said randomly accessed portion of said digital speech data from said memory means to said speech synthesizer means to produce a randomly selected audible request in human speech, operator input means for receiving an operator response to said randomly selected audible request, and means responsive to said digital control data and said operator response to said randomly selected audible request for responding in a manner producing an output indicative of the appropriateness of said operator response with respect to the appropriate operator response corresponding to said randomly selected audible request. 8. A talking electronic apparatus as set forth in claim 1, wherein said means responsive to said digital control data and said operator response is effective to initiate a second selected audible request in human speech via said speech synthesizer means if said operator response to the first selected audible request conforms to the appropriate operator response corresponding thereto.

10. One or more computer-readable media comprising computer-executable instructions for causing a computing device to perform a method for joining extensible markup language (XML) documents, the method comprising: comparing elements of two XML documents by counting a number of occurrences of one or more comparison elements in each of the two XML documents; based on a result of the comparison, identifying that the number of occurrences of the one or more comparison elements in a first XML document of the two XML documents is less than the number of occurrences of the one or more comparison elements in a second XML document of the two XML documents; obtaining join-selection elements from the first XML document, wherein the join-selection elements are a subset of elements in the first XML document; using the join-selection elements, obtaining join-required elements from the second XML document, wherein the join-required elements are required to perform an XML join between the first and second XML documents; and performing the XML join using the elements in the first XML document and the join-required elements from the second XML document.

10. One or more computer-readable media comprising computer-executable instructions for causing a computing device to perform a method for joining extensible markup language (XML) documents, the method comprising: comparing elements of two XML documents by counting a number of occurrences of one or more comparison elements in each of the two XML documents; based on a result of the comparison, identifying that the number of occurrences of the one or more comparison elements in a first XML document of the two XML documents is less than the number of occurrences of the one or more comparison elements in a second XML document of the two XML documents; obtaining join-selection elements from the first XML document, wherein the join-selection elements are a subset of elements in the first XML document; using the join-selection elements, obtaining join-required elements from the second XML document, wherein the join-required elements are required to perform an XML join between the first and second XML documents; and performing the XML join using the elements in the first XML document and the join-required elements from the second XML document. 13. The one or more computer-readable media of claim 10 wherein the first XML document is at a first location and the second XML document is at a different second location, wherein the first and second locations are connected via a network.

1. A computer based interactive, multi-sensory method for teaching students to read words and comprehend passages, comprising the steps of: (a) presenting a menu of teaching components on a screen of a processor based client device associated with a student by a processor based server over a communications network, the server comprising at least a phonics component for teaching students to read through voice and handwriting recognition, the phonics component comprises a plurality of phonics modules for teaching the student an alphabetic code of the English language, each phonics module comprises a different letter category of the alphabetic code and a plurality of exercises for teaching the student the letter category of said each module with a series of multi-sensory interactions with the student; (b) determining and executing a current phonics module associated with the student by phonics component, the letter category of the current phonics module comprises a plurality of letter groups, each letter group comprises at least one of the following letter symbol: a letter, a consonant, a vowel or a syllable; (c) determining, by the phonics component, a current letter group of the current phonics module and a current letter symbol of the current letter group associated with the student; (d) retrieving an exercise for the current letter symbol of the current letter group associated with the student from a database by the phonics component, the exercise comprising at least a visual and auditory drill of the current letter symbol, a writing drill of the current letter symbol and a phonological processing drill; (e) presenting the exercise for the current letter symbol of the current letter group associated with the student on the student's client device by the phonics component to create multi-sensory interactions with the student for the current letter symbol; (f) receiving the student's responses to the multi-sensory interactions of the exercise from the client device by the phonics component over the communications network; (g) processing and scoring the student's responses to the multi-sensory interactions by the phonics component to determine whether the student advances to the next letter symbol of the current letter group or repeats the current letter symbol of the current letter group; (h) storing the student's responses to the multi-sensory interactions and the student's score on the current letter symbol in the database by the phonics component; (i) advancing the student to the next letter symbol of the current letter group by the phonics component if the student's score is greater than or equal to a predetermined threshold and repeating the steps (d)-(h) for the next letter symbol of the current letter group; (j) retrieving another exercise for the current letter symbol of the current letter group by the phonics component and repeating the steps (e)-(h) if the student's score is less than the predetermined threshold; (k) presenting a letter group assessment test of the current letter group on the student's client device by the phonics component upon completion of a last letter symbol of the current letter group, the letter group assessment test comprising at least four parts and further comprising the steps of: performing a first part of the letter group assessment test by the phonics component by performing the following steps: presenting a first set of lists on the student's client device, one list at a time, each list being associated with one different letter symbol of the current letter group and comprising at least two words; for each list on the first set, providing an audio sound of the letter symbol associated with the list on the student's client device and prompting the student to select a word on the list that begins with the audio sound; receiving the student's selections for the first set from the student's client device over the communications network; processing the student's selections for the first set to determine a first assessment score; storing the student's selections from the first set and student's first letter group assessment score in the database; performing a second part of the letter group assessment test by the phonics component by performing the following steps: presenting a second set of lists on the student's client device, one list at a time, each list being associated with one different letter symbol of the current letter group and comprising at least two words; for each list on the second set, providing an audio sound of the letter symbol associated with the list on the student's client device and prompting the student to select a word on the list that ends with the audio sound; receiving the student's selections for the second set from the student's client device over the communications network; processing the student's selections for the second set to determine a second assessment score; storing the student's selections for the second set of lists and student's second letter group assessment score in the database; performing a third part of the letter group assessment test by the phonics component by performing the following steps: presenting a third set of lists on the student's client device, one list at a time, each list being associated with one different letter symbol of the current letter group and comprising at least two words; for each list on the third set, providing an audio sound of the letter symbol associated with the list on the student's client device and prompting the student to select a word on the list that contains the audio sound; receiving the student's selections for the third set from the student's client device over the communications network; processing the student's selections for the third set of lists to determine a third assessment score; storing the student's selections for the third set of lists and student's third letter group assessment score in the database; performing a word per minute timing drill as a fourth part of the letter group assessment test by the phonics component by performing the following steps: presenting a predetermined set of a predetermined number of words on the student's client device, each word being a real or nonsense word comprising at least two letter symbols of the current letter group; for each set, prompting the student to read the words displayed on the student's screen for a predetermined time, preferably one minute; receiving a recording of the words read by the student from the client device over the communications network; analyzing the recording to determine a fourth assessment score comprising three scores, a first score being a total number of words read accurately by the student, a second score being a total number of real words read accurately by the student, and a third score being the total number of nonsense words read accurately, each score of the fourth assessment is determined by comparing the student's pronunciation of the words to correct sounds of the words by a speech recognition engine of the server, the speech recognition engine comprising a library of correct sounds; storing the recording of the words read by the student and the student's fourth assessment score comprising the three scores in the database; (l) processing and scoring the student's responses to the letter group assessment test by the phonics component to determine whether the student advances to the next letter group of the current phonics module or repeats the current letter group of the current phonics module; (m) advancing the student to the next letter group of the current phonics module if the student's letter group assessment score is greater than or equal to a predetermined threshold and repeating the steps (c)-(l) for the next letter group of the current phonics module; and (n) repeating the steps (c)-(l) for the current letter group of the current phonics module if the student's letter group assessment score is less than the predetermined threshold.

1. A computer based interactive, multi-sensory method for teaching students to read words and comprehend passages, comprising the steps of: (a) presenting a menu of teaching components on a screen of a processor based client device associated with a student by a processor based server over a communications network, the server comprising at least a phonics component for teaching students to read through voice and handwriting recognition, the phonics component comprises a plurality of phonics modules for teaching the student an alphabetic code of the English language, each phonics module comprises a different letter category of the alphabetic code and a plurality of exercises for teaching the student the letter category of said each module with a series of multi-sensory interactions with the student; (b) determining and executing a current phonics module associated with the student by phonics component, the letter category of the current phonics module comprises a plurality of letter groups, each letter group comprises at least one of the following letter symbol: a letter, a consonant, a vowel or a syllable; (c) determining, by the phonics component, a current letter group of the current phonics module and a current letter symbol of the current letter group associated with the student; (d) retrieving an exercise for the current letter symbol of the current letter group associated with the student from a database by the phonics component, the exercise comprising at least a visual and auditory drill of the current letter symbol, a writing drill of the current letter symbol and a phonological processing drill; (e) presenting the exercise for the current letter symbol of the current letter group associated with the student on the student's client device by the phonics component to create multi-sensory interactions with the student for the current letter symbol; (f) receiving the student's responses to the multi-sensory interactions of the exercise from the client device by the phonics component over the communications network; (g) processing and scoring the student's responses to the multi-sensory interactions by the phonics component to determine whether the student advances to the next letter symbol of the current letter group or repeats the current letter symbol of the current letter group; (h) storing the student's responses to the multi-sensory interactions and the student's score on the current letter symbol in the database by the phonics component; (i) advancing the student to the next letter symbol of the current letter group by the phonics component if the student's score is greater than or equal to a predetermined threshold and repeating the steps (d)-(h) for the next letter symbol of the current letter group; (j) retrieving another exercise for the current letter symbol of the current letter group by the phonics component and repeating the steps (e)-(h) if the student's score is less than the predetermined threshold; (k) presenting a letter group assessment test of the current letter group on the student's client device by the phonics component upon completion of a last letter symbol of the current letter group, the letter group assessment test comprising at least four parts and further comprising the steps of: performing a first part of the letter group assessment test by the phonics component by performing the following steps: presenting a first set of lists on the student's client device, one list at a time, each list being associated with one different letter symbol of the current letter group and comprising at least two words; for each list on the first set, providing an audio sound of the letter symbol associated with the list on the student's client device and prompting the student to select a word on the list that begins with the audio sound; receiving the student's selections for the first set from the student's client device over the communications network; processing the student's selections for the first set to determine a first assessment score; storing the student's selections from the first set and student's first letter group assessment score in the database; performing a second part of the letter group assessment test by the phonics component by performing the following steps: presenting a second set of lists on the student's client device, one list at a time, each list being associated with one different letter symbol of the current letter group and comprising at least two words; for each list on the second set, providing an audio sound of the letter symbol associated with the list on the student's client device and prompting the student to select a word on the list that ends with the audio sound; receiving the student's selections for the second set from the student's client device over the communications network; processing the student's selections for the second set to determine a second assessment score; storing the student's selections for the second set of lists and student's second letter group assessment score in the database; performing a third part of the letter group assessment test by the phonics component by performing the following steps: presenting a third set of lists on the student's client device, one list at a time, each list being associated with one different letter symbol of the current letter group and comprising at least two words; for each list on the third set, providing an audio sound of the letter symbol associated with the list on the student's client device and prompting the student to select a word on the list that contains the audio sound; receiving the student's selections for the third set from the student's client device over the communications network; processing the student's selections for the third set of lists to determine a third assessment score; storing the student's selections for the third set of lists and student's third letter group assessment score in the database; performing a word per minute timing drill as a fourth part of the letter group assessment test by the phonics component by performing the following steps: presenting a predetermined set of a predetermined number of words on the student's client device, each word being a real or nonsense word comprising at least two letter symbols of the current letter group; for each set, prompting the student to read the words displayed on the student's screen for a predetermined time, preferably one minute; receiving a recording of the words read by the student from the client device over the communications network; analyzing the recording to determine a fourth assessment score comprising three scores, a first score being a total number of words read accurately by the student, a second score being a total number of real words read accurately by the student, and a third score being the total number of nonsense words read accurately, each score of the fourth assessment is determined by comparing the student's pronunciation of the words to correct sounds of the words by a speech recognition engine of the server, the speech recognition engine comprising a library of correct sounds; storing the recording of the words read by the student and the student's fourth assessment score comprising the three scores in the database; (l) processing and scoring the student's responses to the letter group assessment test by the phonics component to determine whether the student advances to the next letter group of the current phonics module or repeats the current letter group of the current phonics module; (m) advancing the student to the next letter group of the current phonics module if the student's letter group assessment score is greater than or equal to a predetermined threshold and repeating the steps (c)-(l) for the next letter group of the current phonics module; and (n) repeating the steps (c)-(l) for the current letter group of the current phonics module if the student's letter group assessment score is less than the predetermined threshold. 6. The method of claim 1 , further comprising the step of performing the phonological processing drill of the current letter symbol on the student's client device by the phonics component by performing the following steps: presenting on the student's client device at least two sets of pictures, one at a time, each set comprising at least two pictures with one picture that starts with the sound of the current letter symbol; providing an audio of the sound that the current letter symbol makes on the student's client device; for each set of pictures, prompting the student to select or identify a picture from the set that starts with the sound the current letter symbol makes; receiving the student's selection of the picture from each set of picture from the student's client device over the communications network; processing the student's picture selections to determine a phonological score; and storing the student's picture selections and student's phonological score in the database.

19. A computer-readable memory device, comprising: a plurality of computer-executable instructions, which, when executed by one or more processors, cause the one or more processors to: identify a group of search result documents that are responsive to a search query; determine a measure of staleness of a search result document in the group of search result documents; determine whether a stale document is preferred for the search query; generate a first score, for the search result document based on: the measure of staleness of the search result document, and whether a stale document is preferred for the search query; generate a second score, for the search result document, that is based on a relevance of the search result document to the search query; combine the first and second scores to generate an overall score; and rank the search result document with regard to at least one other search result document, of the group of search result documents, based on the overall score.

19. A computer-readable memory device, comprising: a plurality of computer-executable instructions, which, when executed by one or more processors, cause the one or more processors to: identify a group of search result documents that are responsive to a search query; determine a measure of staleness of a search result document in the group of search result documents; determine whether a stale document is preferred for the search query; generate a first score, for the search result document based on: the measure of staleness of the search result document, and whether a stale document is preferred for the search query; generate a second score, for the search result document, that is based on a relevance of the search result document to the search query; combine the first and second scores to generate an overall score; and rank the search result document with regard to at least one other search result document, of the group of search result documents, based on the overall score. 20. The computer-readable memory device of claim 19 , where the plurality of computer-executable instructions, which cause the one or more processors to combine the first and second scores, further cause the one or more processors to: adjust the second score by an amount that is based on the first score.

12. The system of claim 11 , wherein the operations caused by the translator further comprise: interpreting the first instance of the mnemonic according to the user defined definition in response to the first mnemonic command comprising a local mnemonic command specifying the mnemonic and the user defined definition, wherein the local mnemonic command specifying the user defined definition overrides any previous mnemonic command of global scope specifying the mnemonic and the translator definition; and interpreting the second instance of the mnemonic according to the translator definition in response to the second mnemonic command comprising a local mnemonic command specifying the mnemonic and the translator definition, wherein the local mnemonic command specifying the translator definition overrides any previous mnemonic command of global scope specifying the mnemonic and the user defined definition.

12. The system of claim 11 , wherein the operations caused by the translator further comprise: interpreting the first instance of the mnemonic according to the user defined definition in response to the first mnemonic command comprising a local mnemonic command specifying the mnemonic and the user defined definition, wherein the local mnemonic command specifying the user defined definition overrides any previous mnemonic command of global scope specifying the mnemonic and the translator definition; and interpreting the second instance of the mnemonic according to the translator definition in response to the second mnemonic command comprising a local mnemonic command specifying the mnemonic and the translator definition, wherein the local mnemonic command specifying the translator definition overrides any previous mnemonic command of global scope specifying the mnemonic and the user defined definition. 13. The system of claim 12 , wherein the operations caused by the translator further comprise: processing a qualified mnemonic in the computer program comprising the mnemonic and a qualification character; interpreting the qualified mnemonic according to the user defined definition for the mnemonic in response to previously processing a local qualifier command associating the qualification character with the user defined definition, wherein a use of the qualification character with the local qualifier command specifying the user defined definition overrides any mnemonic command specifying the mnemonic and the translator definition which precedes the local qualifier command specifying the user defined definition; and interpreting the qualified mnemonic according to the translator definition for the mnemonic in response to previously processing a local qualifier command associating the qualification character with the translator definition, wherein a use of the qualification character with the local qualifier command specifying the translator definition overrides any mnemonic command specifying the mnemonic and the user defined definition which precedes the local qualifier command specifying the translator definition.

1. A method for improving natural language processing conducted by a natural language model, the method comprising: accessing, at a first node in a logical hierarchy configured to guide classification of a plurality of documents by the natural language model, at least one rule associated with the first node, said at least one rule defining a first factor for determining whether a document among the plurality of documents is to be classified into the first node; identifying a percolation criterion associated with a second node in the logical hierarchy that is a parent node to the first node, said percolation criterion indicating that the at least one rule associated with the first node is to be associated also with the second node; based on the identified percolation criterion, associating the at least one rule with the second node such that the at least one rule defines a second factor for determining whether the document is to also be classified into the second node; accessing the document for natural language processing; and classifying the document according to the logical hierarchy by determining whether the document is to be classified into at least one of the second node and the first node based on the at least one rule associated with both the first node and the second node.

1. A method for improving natural language processing conducted by a natural language model, the method comprising: accessing, at a first node in a logical hierarchy configured to guide classification of a plurality of documents by the natural language model, at least one rule associated with the first node, said at least one rule defining a first factor for determining whether a document among the plurality of documents is to be classified into the first node; identifying a percolation criterion associated with a second node in the logical hierarchy that is a parent node to the first node, said percolation criterion indicating that the at least one rule associated with the first node is to be associated also with the second node; based on the identified percolation criterion, associating the at least one rule with the second node such that the at least one rule defines a second factor for determining whether the document is to also be classified into the second node; accessing the document for natural language processing; and classifying the document according to the logical hierarchy by determining whether the document is to be classified into at least one of the second node and the first node based on the at least one rule associated with both the first node and the second node. 2. The method of claim 1 , wherein the percolation criterion is a first percolation criterion, and the method further comprises: identifying a second percolation criterion associated with a third node in the logical hierarchy that is a parent node to the second node, said percolation criterion indicating that the at least one rule associated with the first node is to be associated also with the third node; and based on the identified second percolation criterion, associating the at least one rule with the third node such that the at least one rule defines a third factor for determining whether the document is to also be classified into the third node.

5. The method of claim 1 , wherein determining the weighted overall quality of result statistic comprises: determining a respective difference score for each of the plurality of versions of the document with reference to the reference version of the document, wherein the difference score for a particular version in the plurality of versions of the document and the reference version of the document measures a difference between a representation of the particular version and a representation of the reference version of the document; and weighting each version-specific quality of result statistic by a weight derived from the difference score for the version of the document associated with the version-specific quality of result statistic.

5. The method of claim 1 , wherein determining the weighted overall quality of result statistic comprises: determining a respective difference score for each of the plurality of versions of the document with reference to the reference version of the document, wherein the difference score for a particular version in the plurality of versions of the document and the reference version of the document measures a difference between a representation of the particular version and a representation of the reference version of the document; and weighting each version-specific quality of result statistic by a weight derived from the difference score for the version of the document associated with the version-specific quality of result statistic. 12. The method of claim 5 , wherein the difference score is determined as an inverse of a similarity score, where the similarity score is defined as similarity ⁡ ( A , B ) =  S ⁡ ( A ) ⋂ S ⁡ ( B )   S ⁡ ( A )  , where A is the particular version of the document and B is the reference version of the document.

16. A computer-implemented process for sending a message or document which when viewed by a recipient displays animated alpha-numeric characters on a display screen, comprising using a computer to perform the following process actions for each of at least one character: sending parametric information pertaining to alpha-numeric characters found in the message or document to the recipient along with the message or document characters, said information comprising, for each character to be animated, a position and orientation of at least one straight, geometric dividing line each of which transects the character transversely so as to segment the character into sections, wherein said dividing line position is defined by a distance between a first prescribed point on the dividing line and a prescribed horizontal line associated with the character and said dividing line orientation is defined by an angle of rotation about a second prescribed point, said first prescribed point and second prescribed point being at the same location or different locations, and a location of a rotation point assigned to each of at least one of the character sections; sending animation instructions pertaining to the display of alpha-numeric characters found in the message or document on said display screen to said recipient along with the parametric information, said animation instructions comprising for each section of each character being animated, instructions for displaying the section for each of a series of prescribed-length time periods, said instructions defining if the section is to be displayed, in comparison with its appearance in the immediately preceding time period, with a translation with respect to an adjacent section, or a rotation about a rotation point of the section, or both, or no change as long as there was or will be some change in at least one of the sections during one of the time periods.

16. A computer-implemented process for sending a message or document which when viewed by a recipient displays animated alpha-numeric characters on a display screen, comprising using a computer to perform the following process actions for each of at least one character: sending parametric information pertaining to alpha-numeric characters found in the message or document to the recipient along with the message or document characters, said information comprising, for each character to be animated, a position and orientation of at least one straight, geometric dividing line each of which transects the character transversely so as to segment the character into sections, wherein said dividing line position is defined by a distance between a first prescribed point on the dividing line and a prescribed horizontal line associated with the character and said dividing line orientation is defined by an angle of rotation about a second prescribed point, said first prescribed point and second prescribed point being at the same location or different locations, and a location of a rotation point assigned to each of at least one of the character sections; sending animation instructions pertaining to the display of alpha-numeric characters found in the message or document on said display screen to said recipient along with the parametric information, said animation instructions comprising for each section of each character being animated, instructions for displaying the section for each of a series of prescribed-length time periods, said instructions defining if the section is to be displayed, in comparison with its appearance in the immediately preceding time period, with a translation with respect to an adjacent section, or a rotation about a rotation point of the section, or both, or no change as long as there was or will be some change in at least one of the sections during one of the time periods. 18. The process of claim 16 , wherein the process actions of sending parametric information pertaining to alpha-numeric characters found in the message or document to the recipient along with the message or document characters and sending animation instructions, comprises the actions of: prior to sending said information and instructions, establishing a font type or types the alpha-numeric characters are to exhibit and a behavior category or categories the animation of the alpha-numeric characters are to exhibit for each of one or more triggering characters found in a message or document, wherein said font type dictates what parametric information pertaining to alpha-numeric characters is to be sent along with the message or document characters, and wherein said behavior category dictates the animation instructions that are to be sent along with the message or document characters, and wherein the established font type or types and behavior category or categories are designed to result in animations of the characters in a message or document being displayed which convey the emotions of a message sender to the particular recipient; identifying triggering characters in a message or document being sent and a portion of the message or document that each of the triggering characters applies to; designating the font type or types and the behavior category or categories established for the identified triggering characters; and sending the parametric information pertaining to alpha-numeric characters found in the message or document and the animation instructions, dictated by the designated font type or types and the behavior category or categories, along with the message or document characters for each portion of the message or document that each of the triggering characters applies to.

12. One or more computer-readable storage media storing instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising: obtaining low-level features from each image in a set of images, wherein each image is associated with one or more labels in a set of labels; generating a high-dimensional representation for each image based on the low-level features of the respective image; generating a lower-dimensional representation for each image based on the high-dimensional representation of the respective image; generating a classifier for each label in the set of labels based on the high-dimensional representations of the respective images that are associated with the label or the lower-dimensional representations of the respective images that are associated with the label; and generating a respective combined representation for each image, wherein the respective combined representation of an image includes the respective classifiers of the one or more labels that are associated with the respective image and includes the lower-dimensional representation of the respective image, and wherein the respective combined representation for an image is generated according to {tilde over (x)} ij =λ·w i +(1−λ)· x ij , where x ij denotes the lower-dimensional representation of the image, where {tilde over (x)} ij denotes the respective combined representation of the image, where w i denotes the respective classifiers of the labels that are associated with the image and where λ is a regularization parameter.

12. One or more computer-readable storage media storing instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising: obtaining low-level features from each image in a set of images, wherein each image is associated with one or more labels in a set of labels; generating a high-dimensional representation for each image based on the low-level features of the respective image; generating a lower-dimensional representation for each image based on the high-dimensional representation of the respective image; generating a classifier for each label in the set of labels based on the high-dimensional representations of the respective images that are associated with the label or the lower-dimensional representations of the respective images that are associated with the label; and generating a respective combined representation for each image, wherein the respective combined representation of an image includes the respective classifiers of the one or more labels that are associated with the respective image and includes the lower-dimensional representation of the respective image, and wherein the respective combined representation for an image is generated according to {tilde over (x)} ij =λ·w i +(1−λ)· x ij , where x ij denotes the lower-dimensional representation of the image, where {tilde over (x)} ij denotes the respective combined representation of the image, where w i denotes the respective classifiers of the labels that are associated with the image and where λ is a regularization parameter. 13. The method of claim 12 , wherein the high-dimensional representations include a Fisher vector or a bag of visual features.

1. A computer-based method, comprising: receiving an itinerary query comprising a starting location, an ending location and a duration; identifying a set of trip candidates, from a location-interest graph, comprising: performing a first comparison of the starting location of the itinerary query with at least one of a first starting location of a first trip candidate, a second starting location of a second trip candidate, a third starting location of a third trip candidate or a fourth starting location of a fourth trip candidate; performing a second comparison of the ending location of the itinerary query with at least one of a first ending location of the first trip candidate, a second ending location of the second trip candidate, a third ending location of the third trip candidate or a fourth ending location of the fourth trip candidate; performing a third comparison of the duration of the itinerary query with at least one of: a combination of at least a first travel time associated with the first trip candidate and a first stay time associated with one or more locations associated with the first trip candidate; a combination of at least a second travel time associated with the second trip candidate and a second stay time associated with one or more locations associated with the second trip candidate; a combination of at least a third travel time associated with the third trip candidate and a third stay time associated with one or more locations associated with the third trip candidate; or a combination of at least a fourth travel time associated with the fourth trip candidate and a fourth stay time associated with one or more locations associated with the fourth trip candidate; and including the first trip candidate, the second trip candidate and the third trip candidate, but not the fourth trip candidate, within the set of trip candidates based on the first comparison, the second comparison and the third comparison; identifying: a first threshold difference for the first trip candidate, the first threshold difference comprising a first difference between a desired threshold value and a first value for the first trip candidate; a second threshold difference for the second trip candidate, the second threshold difference comprising a second difference between the desired threshold value and a second value for the second trip candidate; and a third threshold difference for the third trip candidate, the third threshold difference comprising a third difference between the desired threshold value and a third value for the third trip candidate, at least one of the desired threshold value, the first value, the second value or the third threshold value based on one or more trip factors; selecting the first trip candidate and the second trip candidate, but not the third trip candidate, from the set of trip candidates based on the first threshold difference and the second threshold difference corresponding to a desired range of identified threshold differences, and the third threshold difference not corresponding to the desired range of identified threshold differences; ranking the first trip candidate and the second trip candidate based on one or more ranking factors; re-ranking the first trip candidate and the second trip candidate based on one or more historical travel sequences; and providing the re-ranked trip candidates in response to receiving the itinerary query.

1. A computer-based method, comprising: receiving an itinerary query comprising a starting location, an ending location and a duration; identifying a set of trip candidates, from a location-interest graph, comprising: performing a first comparison of the starting location of the itinerary query with at least one of a first starting location of a first trip candidate, a second starting location of a second trip candidate, a third starting location of a third trip candidate or a fourth starting location of a fourth trip candidate; performing a second comparison of the ending location of the itinerary query with at least one of a first ending location of the first trip candidate, a second ending location of the second trip candidate, a third ending location of the third trip candidate or a fourth ending location of the fourth trip candidate; performing a third comparison of the duration of the itinerary query with at least one of: a combination of at least a first travel time associated with the first trip candidate and a first stay time associated with one or more locations associated with the first trip candidate; a combination of at least a second travel time associated with the second trip candidate and a second stay time associated with one or more locations associated with the second trip candidate; a combination of at least a third travel time associated with the third trip candidate and a third stay time associated with one or more locations associated with the third trip candidate; or a combination of at least a fourth travel time associated with the fourth trip candidate and a fourth stay time associated with one or more locations associated with the fourth trip candidate; and including the first trip candidate, the second trip candidate and the third trip candidate, but not the fourth trip candidate, within the set of trip candidates based on the first comparison, the second comparison and the third comparison; identifying: a first threshold difference for the first trip candidate, the first threshold difference comprising a first difference between a desired threshold value and a first value for the first trip candidate; a second threshold difference for the second trip candidate, the second threshold difference comprising a second difference between the desired threshold value and a second value for the second trip candidate; and a third threshold difference for the third trip candidate, the third threshold difference comprising a third difference between the desired threshold value and a third value for the third trip candidate, at least one of the desired threshold value, the first value, the second value or the third threshold value based on one or more trip factors; selecting the first trip candidate and the second trip candidate, but not the third trip candidate, from the set of trip candidates based on the first threshold difference and the second threshold difference corresponding to a desired range of identified threshold differences, and the third threshold difference not corresponding to the desired range of identified threshold differences; ranking the first trip candidate and the second trip candidate based on one or more ranking factors; re-ranking the first trip candidate and the second trip candidate based on one or more historical travel sequences; and providing the re-ranked trip candidates in response to receiving the itinerary query. 10. The method of claim 1 , re-ranking comprising re-ranking merely one or more ranked trip candidates that meet a desired rank threshold.

29. A computer program product embodied on at least one non-transitory computer readable medium and configured to cause at least one hardware processor to operate, the computer program product comprising: code stored on the at least one non-transitory computer readable medium and configured to cause the at least one hardware processor to identify at least one computer-readable Extensible Markup Language (XML)-compliant data document that is eXtensible Business Reporting Language (XBRL)-compliant and includes: a plurality of line items with a plurality of data values, and a plurality of computer-readable semantic tags that describe a semantic meaning of the data values, where the at least one computer-readable XML-compliant data document is capable of including multiple hierarchical relationships between two of the plurality of line items; code stored on the at least one non-transitory computer readable medium and configured to cause the at least one hardware processor to parse the at least one computer-readable XML-compliant data document, by: receiving the at least one computer-readable XML-compliant data document, identifying the multiple hierarchical relationships between the two line items, and at least one of the computer-readable semantic tags that describes the semantic meaning of at least one of the data values included in the at least one computer-readable XML-compliant data document; code stored on the at least one non-transitory computer readable medium and configured to cause the at least one hardware processor to access a plurality of computer-readable rules including: a computer-readable datatype rule for validation of a type of data values, a computer-readable calculation rule for validation of a calculation involving data values, and a computer-readable unit rule for validation of a unit of data values; code stored on the at least one non-transitory computer readable medium and configured to cause the at least one hardware processor to process the at least one computer-readable XML-compliant data document, by: identifying at least a subset of the computer-readable rules including at least one of: the computer-readable datatype rule for validation of the type of data values, the computer-readable calculation rule for validation of the calculation involving data values, or the computer-readable unit rule for validation of the unit of data values; and processing at least a portion of the data values of at least a portion of the plurality of line items of the at least one computer-readable XML-compliant data document, utilizing the at least subset of the computer-readable rules, and at least a portion of the computer-readable semantic tags of the at least one computer-readable XML-compliant data document; code stored on the at least one non-transitory computer readable medium and configured to cause the at least one hardware processor to display a result of a validation of the at least one computer-readable XML-compliant data document; code stored on the at least one non-transitory computer readable medium and configured to cause the at least one hardware processor to develop a report, by: identifying the at least one computer-readable semantic tag that describes the semantic meaning of the at least one data value included in the at least one computer-readable XML-compliant data document, and retrieving data from one or more sources to represent the at least one data value in the report.

29. A computer program product embodied on at least one non-transitory computer readable medium and configured to cause at least one hardware processor to operate, the computer program product comprising: code stored on the at least one non-transitory computer readable medium and configured to cause the at least one hardware processor to identify at least one computer-readable Extensible Markup Language (XML)-compliant data document that is eXtensible Business Reporting Language (XBRL)-compliant and includes: a plurality of line items with a plurality of data values, and a plurality of computer-readable semantic tags that describe a semantic meaning of the data values, where the at least one computer-readable XML-compliant data document is capable of including multiple hierarchical relationships between two of the plurality of line items; code stored on the at least one non-transitory computer readable medium and configured to cause the at least one hardware processor to parse the at least one computer-readable XML-compliant data document, by: receiving the at least one computer-readable XML-compliant data document, identifying the multiple hierarchical relationships between the two line items, and at least one of the computer-readable semantic tags that describes the semantic meaning of at least one of the data values included in the at least one computer-readable XML-compliant data document; code stored on the at least one non-transitory computer readable medium and configured to cause the at least one hardware processor to access a plurality of computer-readable rules including: a computer-readable datatype rule for validation of a type of data values, a computer-readable calculation rule for validation of a calculation involving data values, and a computer-readable unit rule for validation of a unit of data values; code stored on the at least one non-transitory computer readable medium and configured to cause the at least one hardware processor to process the at least one computer-readable XML-compliant data document, by: identifying at least a subset of the computer-readable rules including at least one of: the computer-readable datatype rule for validation of the type of data values, the computer-readable calculation rule for validation of the calculation involving data values, or the computer-readable unit rule for validation of the unit of data values; and processing at least a portion of the data values of at least a portion of the plurality of line items of the at least one computer-readable XML-compliant data document, utilizing the at least subset of the computer-readable rules, and at least a portion of the computer-readable semantic tags of the at least one computer-readable XML-compliant data document; code stored on the at least one non-transitory computer readable medium and configured to cause the at least one hardware processor to display a result of a validation of the at least one computer-readable XML-compliant data document; code stored on the at least one non-transitory computer readable medium and configured to cause the at least one hardware processor to develop a report, by: identifying the at least one computer-readable semantic tag that describes the semantic meaning of the at least one data value included in the at least one computer-readable XML-compliant data document, and retrieving data from one or more sources to represent the at least one data value in the report. 39. The computer program product of claim 29 , wherein the computer program product is configured such that the semantic meaning of the data values is searchable.

4. A system comprising: a processor; a memory communicatively coupled to the processor, the memory having stored therein computer-executable instructions configured to implement the system, including: means for computing a statistical predictive model of a user's web page revisit interval based at least on the user's historical pattern of access to a web page, the user's pattern of access is determined using data collected in connection with a plurality of the user's past accesses to the web site; means for using inferred user revisit intervals derived from the predictive model to distinguish between an automated refresh of the web page and a request for access to the web page by the user; and means for determining a probability of information associated with the web page having value to a user given evidence of the user's interest in the web page, the probability formulated as: Pr(Information Value |E±, Ez, . . . Ej_), wherein Pr is the probability, Information Value relates to an importance of the site and topic to the user given evidence F relating to attributes of information importance, and J being an integer, the evidence including at least one of: how much time the user spends observing the web page; higher-level topic of the web page viewed; interactivity with the web page; and a consideration of navigation efforts of a user to navigate to the web page; means for comparing a log of accesses to the web page with a statistical mode of a probability distribution of the use's revisit intervals for a respective URL; and means for discarding accesses in the loci that are within a tolerance of the statistical mode when at least 10% of the intervals between logged accesses belong to the mode.

4. A system comprising: a processor; a memory communicatively coupled to the processor, the memory having stored therein computer-executable instructions configured to implement the system, including: means for computing a statistical predictive model of a user's web page revisit interval based at least on the user's historical pattern of access to a web page, the user's pattern of access is determined using data collected in connection with a plurality of the user's past accesses to the web site; means for using inferred user revisit intervals derived from the predictive model to distinguish between an automated refresh of the web page and a request for access to the web page by the user; and means for determining a probability of information associated with the web page having value to a user given evidence of the user's interest in the web page, the probability formulated as: Pr(Information Value |E±, Ez, . . . Ej_), wherein Pr is the probability, Information Value relates to an importance of the site and topic to the user given evidence F relating to attributes of information importance, and J being an integer, the evidence including at least one of: how much time the user spends observing the web page; higher-level topic of the web page viewed; interactivity with the web page; and a consideration of navigation efforts of a user to navigate to the web page; means for comparing a log of accesses to the web page with a statistical mode of a probability distribution of the use's revisit intervals for a respective URL; and means for discarding accesses in the loci that are within a tolerance of the statistical mode when at least 10% of the intervals between logged accesses belong to the mode. 5. The system of claim 4 , further comprising: means for recording at least one context relating to the user's historical pattern of access; and means for determining future access patterns based at least in part on the historical pattern of access and the recorded context.

15. A speech recognition system, comprising: a conferencing database; a model selection unit which selects a recognition model for each user of a plurality of users based on subject information and language information of each user that are stored in said conferencing database as part of characteristic information, and which selects translation dictionary information based on said language information of each user and language information for translation that are stored in said conferencing database as part of said characteristic information; a speech recognition unit which translates input speech into text data based on said selected recognition model; and a translation unit which translates said text data based on said selected translation dictionary information.

15. A speech recognition system, comprising: a conferencing database; a model selection unit which selects a recognition model for each user of a plurality of users based on subject information and language information of each user that are stored in said conferencing database as part of characteristic information, and which selects translation dictionary information based on said language information of each user and language information for translation that are stored in said conferencing database as part of said characteristic information; a speech recognition unit which translates input speech into text data based on said selected recognition model; and a translation unit which translates said text data based on said selected translation dictionary information. 17. The speech recognition system according to claim 15 , wherein said language information for translation includes language information of translation origin and language information of translation destination.

1. A system comprising: one or more non-transitory computer readable storage media; one or more processors; and one or more modules stored in the one or more non-transitory computer readable storage media and executed by the one or more processors to: associate a shared electronic session with chat activity of a first user and a second user and a first browsing activity of the first user and a second browsing activity of the second user, wherein the first browsing activity and the second browsing activity are each associated with one or more items available for purchase via an electronic commerce website; transmit, to the first user, an indicator representing the second browsing activity of the second user, wherein the second browsing activity of the second user is independent of the first browsing activity of the first user; determine a sentiment of at least the first user or the second user with respect to one or more items associated with the browsing activity of the first user or the second user based at least in part on performing sentiment analysis on the chat activity; and recommend at least one item of the one or more items to the at least the first user or the second user.

1. A system comprising: one or more non-transitory computer readable storage media; one or more processors; and one or more modules stored in the one or more non-transitory computer readable storage media and executed by the one or more processors to: associate a shared electronic session with chat activity of a first user and a second user and a first browsing activity of the first user and a second browsing activity of the second user, wherein the first browsing activity and the second browsing activity are each associated with one or more items available for purchase via an electronic commerce website; transmit, to the first user, an indicator representing the second browsing activity of the second user, wherein the second browsing activity of the second user is independent of the first browsing activity of the first user; determine a sentiment of at least the first user or the second user with respect to one or more items associated with the browsing activity of the first user or the second user based at least in part on performing sentiment analysis on the chat activity; and recommend at least one item of the one or more items to the at least the first user or the second user. 2. The system of claim 1 , wherein the one or more modules are further executed by the one or more processors to enable the first user or the second user to purchase an item of the one or more items.

34. The computer program as recited in claim 32 , wherein the electronic document is part of a test data set, and further comprising: a code segment for providing the training data set from at least one training document having a manually-constructed collection of keywords; a code segment for identifying a training set of candidate entries in the training data set; a code segment for constructing a training feature vector for each candidate entry in the training data set, wherein the training feature vector comprises at least one feature selected from the group consisting of a discourse comprehension feature, a part-of-speech pattern feature, and an encyclopedic annotation feature, and wherein the training feature vector further comprises a label indicating presence or absence of the candidate entry in the manually-constructed collection of keywords for that training document; a code segment for running a machine learning algorithm on the training data set using the training feature vectors and the training set of candidate entries; a code segment for assigning a numeric score to each candidate entry in the test data set by applying the trained machine learning algorithm to the feature vectors for the candidate entries in the test data set; a code segment for classifying the candidate entries in the test data set using the numeric score and the trained machine learning algorithm as belonging to the keywords or not; and a code segment for selecting a specified number of entries to be retained as the keywords for the electronic document.

34. The computer program as recited in claim 32 , wherein the electronic document is part of a test data set, and further comprising: a code segment for providing the training data set from at least one training document having a manually-constructed collection of keywords; a code segment for identifying a training set of candidate entries in the training data set; a code segment for constructing a training feature vector for each candidate entry in the training data set, wherein the training feature vector comprises at least one feature selected from the group consisting of a discourse comprehension feature, a part-of-speech pattern feature, and an encyclopedic annotation feature, and wherein the training feature vector further comprises a label indicating presence or absence of the candidate entry in the manually-constructed collection of keywords for that training document; a code segment for running a machine learning algorithm on the training data set using the training feature vectors and the training set of candidate entries; a code segment for assigning a numeric score to each candidate entry in the test data set by applying the trained machine learning algorithm to the feature vectors for the candidate entries in the test data set; a code segment for classifying the candidate entries in the test data set using the numeric score and the trained machine learning algorithm as belonging to the keywords or not; and a code segment for selecting a specified number of entries to be retained as the keywords for the electronic document. 35. The computer program as recited in claim 34 , wherein the machine learning algorithm is selected from the group consisting of Naïve Bayes, Support Vector Machine, Relevance Vector Machine, decision tree, genetic algorithm, rule induction, k-Nearest Neighbors, Gaussian, Gaussian Mixture Model, artificial neural network, multilayer perceptron, and radial basis function network.

1. A method, comprising: providing, by a device, a dimension hierarchy file, the dimension hierarchy file being generated based on at least one data source, the at least one data source comprising a plurality of items, the dimension hierarchy file including a plurality of dimension nodes, the plurality of dimension nodes corresponding to the respective plurality of items of the at least one data source, and each of the plurality of dimension nodes including information identifying at least one property associated with each of the plurality of items, and the dimension hierarchy file being preprocessed to produce an index of the at least one data source; receiving, by the device, a text string from a user device; querying, by the device and based on the text string, the dimension hierarchy file using the index to identify at least one dimension node of the plurality of dimension nodes, the at least one dimension node identifying at least one suggested search item of the plurality of items; and providing, by the device, the at least one suggested search item for generating results for display on the user device, the results including information identifying the at least one property associated with the at least one suggested search item.

1. A method, comprising: providing, by a device, a dimension hierarchy file, the dimension hierarchy file being generated based on at least one data source, the at least one data source comprising a plurality of items, the dimension hierarchy file including a plurality of dimension nodes, the plurality of dimension nodes corresponding to the respective plurality of items of the at least one data source, and each of the plurality of dimension nodes including information identifying at least one property associated with each of the plurality of items, and the dimension hierarchy file being preprocessed to produce an index of the at least one data source; receiving, by the device, a text string from a user device; querying, by the device and based on the text string, the dimension hierarchy file using the index to identify at least one dimension node of the plurality of dimension nodes, the at least one dimension node identifying at least one suggested search item of the plurality of items; and providing, by the device, the at least one suggested search item for generating results for display on the user device, the results including information identifying the at least one property associated with the at least one suggested search item. 2. The method of claim 1 , where each of the plurality of dimension nodes further includes at least one synonym associated with each of the plurality of items, and where querying the dimension hierarchy file using the index to identify the at least one dimension node comprises: querying the dimension hierarchy file using the index to identify the at least one dimension node based on the at least one synonym associated with the at least one dimension node including the text string.

10. A method comprising: receiving a request, over a network at a social shopping platform, from a user in a first community of users, the social shopping platform including a plurality of network-based marketplaces respectively associated with a plurality of communities, the plurality of communities including the first community of users that is associated with a first network-based marketplace; identifying the first network-based marketplace from the plurality of network-based marketplaces based on the request, the request for an activity associated with a listing for sale in the first network-based marketplace that is used by the first community of users to transact listings that describe items of a single domain that is of interest to the first community of users; updating a listing reputation score for the listing based on a user reputation score for the user and based upon the activity associated with the listing; and updating the user reputation score based on the listing reputation score.

10. A method comprising: receiving a request, over a network at a social shopping platform, from a user in a first community of users, the social shopping platform including a plurality of network-based marketplaces respectively associated with a plurality of communities, the plurality of communities including the first community of users that is associated with a first network-based marketplace; identifying the first network-based marketplace from the plurality of network-based marketplaces based on the request, the request for an activity associated with a listing for sale in the first network-based marketplace that is used by the first community of users to transact listings that describe items of a single domain that is of interest to the first community of users; updating a listing reputation score for the listing based on a user reputation score for the user and based upon the activity associated with the listing; and updating the user reputation score based on the listing reputation score. 11. The method of claim 10 , further comprising generating a user interface that displays the listing reputation score.

1. A computer-implemented method comprising: receiving, by at least one processor, digital graphic novel content; producing, by at least one processor, a numerical map that represents an image extracted from the digital graphic novel content; responsive to inputting the numerical map into a first artificial neural network of a machine learning model configured to determine regions of the digital graphic novel content that are likely to include speech bubbles, receiving, by the at least one processor, from the first artificial neural network, a plurality of candidate regions of the digital graphic novel content that are likely to include speech bubbles; and responsive to inputting the plurality of candidate regions into a second artificial neural network of the machine learning model, receiving, by at least one processor, from the second artificial neural network, features of the digital graphic novel content that include a plurality of speech bubbles containing text; generating, by at least one processor, based on the features of the digital graphic novel content, contextual information corresponding to the features, the contextual information including the text of the plurality of speech bubbles in an intended reading order of the plurality of speech bubbles; and automatically translating, by at least one processor, based at least in part on the contextual information, from a first natural language to a second natural language, the text contained in the plurality of speech bubbles to produce translated text.

1. A computer-implemented method comprising: receiving, by at least one processor, digital graphic novel content; producing, by at least one processor, a numerical map that represents an image extracted from the digital graphic novel content; responsive to inputting the numerical map into a first artificial neural network of a machine learning model configured to determine regions of the digital graphic novel content that are likely to include speech bubbles, receiving, by the at least one processor, from the first artificial neural network, a plurality of candidate regions of the digital graphic novel content that are likely to include speech bubbles; and responsive to inputting the plurality of candidate regions into a second artificial neural network of the machine learning model, receiving, by at least one processor, from the second artificial neural network, features of the digital graphic novel content that include a plurality of speech bubbles containing text; generating, by at least one processor, based on the features of the digital graphic novel content, contextual information corresponding to the features, the contextual information including the text of the plurality of speech bubbles in an intended reading order of the plurality of speech bubbles; and automatically translating, by at least one processor, based at least in part on the contextual information, from a first natural language to a second natural language, the text contained in the plurality of speech bubbles to produce translated text. 4. The computer-implemented method of claim 1 , further comprising: creating a packaged digital graphic novel including the digital graphic novel content and presentation metadata, the presentation metadata including the translated text and an indication of the at least one feature of the features of the digital graphic novel to which the translated text corresponds; and providing, by the at least one processor, the packaged digital graphic novel to a reader device that is configured to present the digital graphic novel content in a manner in accordance with the presentation metadata.

18. The method of claim 17 wherein the content of the message text is dynamically generated using content from the data object.

18. The method of claim 17 wherein the content of the message text is dynamically generated using content from the data object. 19. The method of claim 18 wherein the confirmation message window includes onscreen buttons, one of the onscreen buttons for accepting the data object, one of the onscreen buttons for editing the data object, and one of the onscreen buttons for cancelling the data object, the method comprising performing an action associated with one of the onscreen buttons in response to selecting input selecting one of the onscreen buttons.

9. A computer program product for selecting a recommended investment portfolio based in part on investment factor considerations, the computer program product comprising computer-readable media encoded with non-transitory tangible instructions for execution by a processor to perform a method comprising: (a) providing a portfolio of securities identified for potential inclusion in the recommended investment portfolio; (b) providing investment factor scores for the identified securities; (c) ranking the identified securities relative to each other based on their investment factor scores in a computerized ranking engine; (d) entering into a processor: (i) initial weightings for each of the identified securities, or data by which initial weightings for each of the identified securities is objectively calculated, the initial weightings or the data being unadjusted by investment factor considerations, (ii) the ranking of the identified securities based on their investment factor scores, and (iii) an investment factor multiplier algorithm that is correlated with the relative ranking; (e) calculating via the processor using a weighting engine, adjusted weightings for the portfolio of securities using at least the entered items (i)-(iii), wherein securities having higher ranked investment factor scores relative to other securities receive greater weightings, and the weightings include non-binary weightings; (f) outputting via the processor, the adjusted weightings for the portfolio of securities; and (g) selecting the recommended investment portfolio based in part on investment factor considerations using the adjusted weightings.

9. A computer program product for selecting a recommended investment portfolio based in part on investment factor considerations, the computer program product comprising computer-readable media encoded with non-transitory tangible instructions for execution by a processor to perform a method comprising: (a) providing a portfolio of securities identified for potential inclusion in the recommended investment portfolio; (b) providing investment factor scores for the identified securities; (c) ranking the identified securities relative to each other based on their investment factor scores in a computerized ranking engine; (d) entering into a processor: (i) initial weightings for each of the identified securities, or data by which initial weightings for each of the identified securities is objectively calculated, the initial weightings or the data being unadjusted by investment factor considerations, (ii) the ranking of the identified securities based on their investment factor scores, and (iii) an investment factor multiplier algorithm that is correlated with the relative ranking; (e) calculating via the processor using a weighting engine, adjusted weightings for the portfolio of securities using at least the entered items (i)-(iii), wherein securities having higher ranked investment factor scores relative to other securities receive greater weightings, and the weightings include non-binary weightings; (f) outputting via the processor, the adjusted weightings for the portfolio of securities; and (g) selecting the recommended investment portfolio based in part on investment factor considerations using the adjusted weightings. 13. The computer program product of claim 9 wherein the instructions when executed by the processor perform a method further comprising: (h) defining an index based on the recommended investment portfolio.

1. A computerized device comprising: a processor; and a user interface operatively connected to said processor, said user interface receiving a question comprising question terms, said processor automatically searching sources of data containing passages to produce candidate answers to said question, said searching being based on said question terms, and said searching identifying passages that support each of said candidate answers based on scoring features that indicate whether said candidate answers are correct answers to said question, said processor automatically creating a scoring feature-specific matrix for each scoring feature of said scoring features, each said scoring feature-specific matrix specifying all different combinations of said passages, said candidate answers, and said question terms as vectors and comprising score fields for score values for each specific question term with respect to a specific passage and a specific candidate answer, and each score field containing a score value corresponding to a vector and indicating how a passage term of said specific passage aligns with said specific question term to support said specific candidate answer as being a correct answer to said question; said processor automatically combining said vectors by calculating a statistical measure of said vectors to produce a collapsed score for each of said question terms, said statistical measure comprising a collapsing function; said processor automatically combining collapsed scores to produce a combined score for each of said candidate answers; and said processor automatically ranking said candidate answers based on each said score value.

1. A computerized device comprising: a processor; and a user interface operatively connected to said processor, said user interface receiving a question comprising question terms, said processor automatically searching sources of data containing passages to produce candidate answers to said question, said searching being based on said question terms, and said searching identifying passages that support each of said candidate answers based on scoring features that indicate whether said candidate answers are correct answers to said question, said processor automatically creating a scoring feature-specific matrix for each scoring feature of said scoring features, each said scoring feature-specific matrix specifying all different combinations of said passages, said candidate answers, and said question terms as vectors and comprising score fields for score values for each specific question term with respect to a specific passage and a specific candidate answer, and each score field containing a score value corresponding to a vector and indicating how a passage term of said specific passage aligns with said specific question term to support said specific candidate answer as being a correct answer to said question; said processor automatically combining said vectors by calculating a statistical measure of said vectors to produce a collapsed score for each of said question terms, said statistical measure comprising a collapsing function; said processor automatically combining collapsed scores to produce a combined score for each of said candidate answers; and said processor automatically ranking said candidate answers based on each said score value. 4. The computerized device according to claim 1 , said collapsing function calculating one of a maximum, a minimum, a sum, a mean, a median, and a standard-deviation of said vectors.

1. A method for controlling the response to spoken language input, comprising: receiving user data from a device; receiving a first spoken language input from the device; identifying tags within the first spoken language input; searching a knowledge base framework based on the tags and the user data, wherein the knowledge base framework is a database that includes a plurality of entities, attributes, and relationships between the entities and the attributes; identifying entities, attributes, and relationship within the knowledge base framework that match at least one of the tags and the user data; creating a state graph based on a portion of the knowledge base framework that includes any matched entities, matched attributes, and identified relationships and based on the tags, wherein the state graph is created at least in part by transforming the portion of the knowledge base framework into a probabilistic model graph by replacing the identified relationships with weighted connections and by assigning a confidence indicator to each node of the state graph; determining at least one goal based on the state graph; and sending instructions to perform an action to the device based on the at least one goal, the weighted connections, and the confidence indicators.

1. A method for controlling the response to spoken language input, comprising: receiving user data from a device; receiving a first spoken language input from the device; identifying tags within the first spoken language input; searching a knowledge base framework based on the tags and the user data, wherein the knowledge base framework is a database that includes a plurality of entities, attributes, and relationships between the entities and the attributes; identifying entities, attributes, and relationship within the knowledge base framework that match at least one of the tags and the user data; creating a state graph based on a portion of the knowledge base framework that includes any matched entities, matched attributes, and identified relationships and based on the tags, wherein the state graph is created at least in part by transforming the portion of the knowledge base framework into a probabilistic model graph by replacing the identified relationships with weighted connections and by assigning a confidence indicator to each node of the state graph; determining at least one goal based on the state graph; and sending instructions to perform an action to the device based on the at least one goal, the weighted connections, and the confidence indicators. 9. The method of claim 1 , wherein at least one of the tags include a user intent and a contradictory tag.

1. A voice retrieval apparatus comprising: a display; a memory; and a processor that executes the following processes: a voice recording process of storing recorded voices in the memory; an accepting process of accepting a retrieval term; a retrieval process of retrieving, from the recorded voices, a plurality of candidate segments where an utterance of the accepted retrieval term is estimated; a first display control process of displaying a plurality of pieces of candidate identifying information respectively corresponding to the plurality of pieces of candidate segments on the display in an order of likelihood; a first replay process of replaying voices in a candidate segment corresponding to a piece of candidate identifying information that is identified in accordance with a first user operation by which the piece of candidate identifying information is defined from the plurality of the pieces of candidate identifying information; a user operation accepting process of accepting a second user operation by which a piece of candidate identifying information is identified from the plurality of the pieces of candidate identifying information after the voices in the candidate segment are replayed; and a second display control process of adding a marking to a portion of display information indicating a time transition of the recorded voices, the portion of the display information corresponding to the piece of the candidate identifying information identified by the second user operation, and displaying the display information with the marking on the display, in accordance with a third user operation.

1. A voice retrieval apparatus comprising: a display; a memory; and a processor that executes the following processes: a voice recording process of storing recorded voices in the memory; an accepting process of accepting a retrieval term; a retrieval process of retrieving, from the recorded voices, a plurality of candidate segments where an utterance of the accepted retrieval term is estimated; a first display control process of displaying a plurality of pieces of candidate identifying information respectively corresponding to the plurality of pieces of candidate segments on the display in an order of likelihood; a first replay process of replaying voices in a candidate segment corresponding to a piece of candidate identifying information that is identified in accordance with a first user operation by which the piece of candidate identifying information is defined from the plurality of the pieces of candidate identifying information; a user operation accepting process of accepting a second user operation by which a piece of candidate identifying information is identified from the plurality of the pieces of candidate identifying information after the voices in the candidate segment are replayed; and a second display control process of adding a marking to a portion of display information indicating a time transition of the recorded voices, the portion of the display information corresponding to the piece of the candidate identifying information identified by the second user operation, and displaying the display information with the marking on the display, in accordance with a third user operation. 5. The voice retrieval apparatus according to claim 1 , wherein the display information is a speech waveform.

1. A method comprising: determining an estimated number of items that match a first structured query by a computing device, wherein the first structured query comprises one or more attributes values and each attribute value is associated with an attribute; determining if the estimated number of items is below a threshold number of items by the computing device; and if the estimated number of items is below the threshold number of items: determining a plurality of candidate structured queries from the first structured query by the computing device, wherein the number of candidate structured queries in the plurality of candidate structured queries is proportional to a maximum time, wherein the number of candidate structured queries is determined by dividing the maximum time by the expected amount of time it takes to determine and evaluate a candidate structured query; for each candidate structured query of the candidate structured queries, estimating a distance between an item that matches the candidate structured query and the first structured query based on the attribute values associated with the first structured query, one or more attribute values associated with the item that matched the candidate structured query, and popularity information associated with the item that matched the candidate structured query by the computing device; determining the candidate structured query with a smallest determined distance as a second structured query by the computing device; determining a plurality of items that match the second structured query by the computing device; and providing indicators of each of the determined plurality of items by the computing device through a network.

1. A method comprising: determining an estimated number of items that match a first structured query by a computing device, wherein the first structured query comprises one or more attributes values and each attribute value is associated with an attribute; determining if the estimated number of items is below a threshold number of items by the computing device; and if the estimated number of items is below the threshold number of items: determining a plurality of candidate structured queries from the first structured query by the computing device, wherein the number of candidate structured queries in the plurality of candidate structured queries is proportional to a maximum time, wherein the number of candidate structured queries is determined by dividing the maximum time by the expected amount of time it takes to determine and evaluate a candidate structured query; for each candidate structured query of the candidate structured queries, estimating a distance between an item that matches the candidate structured query and the first structured query based on the attribute values associated with the first structured query, one or more attribute values associated with the item that matched the candidate structured query, and popularity information associated with the item that matched the candidate structured query by the computing device; determining the candidate structured query with a smallest determined distance as a second structured query by the computing device; determining a plurality of items that match the second structured query by the computing device; and providing indicators of each of the determined plurality of items by the computing device through a network. 9. The method of claim 1 , wherein the plurality of candidate structured queries is determined from the first structured query using a dynamic programming heuristic.

1. An electronic content generation and distribution method for generating and distributing audio content from a plurality of written articles, the method comprising: receiving a plurality of time-sensitive written articles for audio narration by a narrator; for each of the articles, using a narrator selection engine for determining a narrator based on: familiarity with the written article, availability of the narrator in view of the article being time-sensitive, and the narrator's exposure to a plurality of listeners; wherein for each of the articles, assigning the written articles to the determined narrators; electronically distributing-the written articles to the determined narrators, so that for each of the written articles, the narrator generates an audio narration within a defined time period based on the content of the written article being time-sensitive, the narrator generating the audio narration for receiving exposure to the plurality of listeners for seeking further engagement with one or more of the plurality of listeners; electronically receiving the audio narrations from the narrators; assigning a plurality of content identifiers to the audio narration based on the written articles and the narrator; accessing a subscriber database including subscriber information and subscriber preference data; electronically comparing the plurality of content identifiers to the subscriber preference data to determine a distribution list for the audio narrations; distributing the audio narration to subscribers designated in the distribution list so that the subscriber can engage the audio narration via a content interface application; and facilitating engagement between the narrator and the subscriber through inclusion of narrator identifying information with the audio narration, including contact information for the subscriber to view narrator background information and directly communicate with the narrator regarding further engagement with the narrator.

1. An electronic content generation and distribution method for generating and distributing audio content from a plurality of written articles, the method comprising: receiving a plurality of time-sensitive written articles for audio narration by a narrator; for each of the articles, using a narrator selection engine for determining a narrator based on: familiarity with the written article, availability of the narrator in view of the article being time-sensitive, and the narrator's exposure to a plurality of listeners; wherein for each of the articles, assigning the written articles to the determined narrators; electronically distributing-the written articles to the determined narrators, so that for each of the written articles, the narrator generates an audio narration within a defined time period based on the content of the written article being time-sensitive, the narrator generating the audio narration for receiving exposure to the plurality of listeners for seeking further engagement with one or more of the plurality of listeners; electronically receiving the audio narrations from the narrators; assigning a plurality of content identifiers to the audio narration based on the written articles and the narrator; accessing a subscriber database including subscriber information and subscriber preference data; electronically comparing the plurality of content identifiers to the subscriber preference data to determine a distribution list for the audio narrations; distributing the audio narration to subscribers designated in the distribution list so that the subscriber can engage the audio narration via a content interface application; and facilitating engagement between the narrator and the subscriber through inclusion of narrator identifying information with the audio narration, including contact information for the subscriber to view narrator background information and directly communicate with the narrator regarding further engagement with the narrator. 19. The method of claim 1 , wherein the facilitating engagement with the narrator includes at least one of: an audio introduction by the narrator; and a link to an electronic profile page of the narrator.

1. A method for processing and analyzing content from at least one Website on a communication network, the method comprising steps for: receiving instructions, via a graphical user interface (GUI) executed by a server, to conduct a search for at least one keyword on the communication network; launching a Web crawler, by the server, to search a plurality of Websites for at least one Website having content that includes said at least one keyword; identifying Websites comprising at least one comment container, wherein said comment container includes at least one conversation; creating a unique xpath, by the server, to said at least one comment container of each identified Website of said Websites, wherein the unique xpath comprises an extraction string dynamically generated to identify at least one of how to find comments, the date of a post on the Website, IP address, user name, and location; saving, in a database, the unique xpath created for said at least one comment container of said each identified Website; detecting using the unique xpath at least one conversation that includes the at least one keyword from search of a content of said comment container of said each Website accessible only by said unique xpath; saving only a portion of the content of said comment container from said each Website that includes the at least one keyword along with information associated to each conversation of said identified conversation; assigning, via a server, a categorical topic for said each conversation; assigning, via a server, a sentiment for said each conversation; and generating at least one report, by the server, providing selected information related to said each conversation, along with categorical topic and sentiment for said each conversation, wherein the method is computer implemented, and wherein said assigning the sentiment comprises assigning a positive or negative value along a range, based on the words comprised in the content of the Website.

1. A method for processing and analyzing content from at least one Website on a communication network, the method comprising steps for: receiving instructions, via a graphical user interface (GUI) executed by a server, to conduct a search for at least one keyword on the communication network; launching a Web crawler, by the server, to search a plurality of Websites for at least one Website having content that includes said at least one keyword; identifying Websites comprising at least one comment container, wherein said comment container includes at least one conversation; creating a unique xpath, by the server, to said at least one comment container of each identified Website of said Websites, wherein the unique xpath comprises an extraction string dynamically generated to identify at least one of how to find comments, the date of a post on the Website, IP address, user name, and location; saving, in a database, the unique xpath created for said at least one comment container of said each identified Website; detecting using the unique xpath at least one conversation that includes the at least one keyword from search of a content of said comment container of said each Website accessible only by said unique xpath; saving only a portion of the content of said comment container from said each Website that includes the at least one keyword along with information associated to each conversation of said identified conversation; assigning, via a server, a categorical topic for said each conversation; assigning, via a server, a sentiment for said each conversation; and generating at least one report, by the server, providing selected information related to said each conversation, along with categorical topic and sentiment for said each conversation, wherein the method is computer implemented, and wherein said assigning the sentiment comprises assigning a positive or negative value along a range, based on the words comprised in the content of the Website. 9. The method of claim 1 , wherein the xpath comprises a programming structure to extract comments and/or associated data from the Website.

1. An information-processing apparatus, comprising: communication circuitry configured to receive a telephone call from a telephone number of a calling party, transmit the telephone number to a search engine on the Internet when the telephone call is received, and receive a result from the search engine after the telephone number is transmitted; and a processor configured to determine whether the telephone call from the telephone number should be conducted with the calling party based on a textual search of the result from the search engine for a plurality of predetermined words.

1. An information-processing apparatus, comprising: communication circuitry configured to receive a telephone call from a telephone number of a calling party, transmit the telephone number to a search engine on the Internet when the telephone call is received, and receive a result from the search engine after the telephone number is transmitted; and a processor configured to determine whether the telephone call from the telephone number should be conducted with the calling party based on a textual search of the result from the search engine for a plurality of predetermined words. 2. The information-processing apparatus according to claim 1 , wherein the processor is configured to determine whether the information-processing apparatus is in proximity to a vehicle.

5. The method of claim 1 , wherein each of the multiple feature points comprises a set of pixels that are located adjacent to a visual feature in an image.

5. The method of claim 1 , wherein each of the multiple feature points comprises a set of pixels that are located adjacent to a visual feature in an image. 6. The method of claim 5 , wherein the set of pixels that are located adjacent to the visual feature surround the visual feature.

1. A computer program product for automatically generating a narrative story using data and information, the computer program product comprising: a plurality of angle data structures that are readable by a processor and resident on a non-transitory computer-readable storage medium, each of the plurality of angle data structures comprising a plurality of data representations that connect a plurality of events, circumstances, and/or entities as a model of a thematic nature and not comprising specific text for a narrative story, each angle data structure being associated with at least one of a plurality of applicability conditions; and a plurality of instructions that are resident on a non-transitory computer-readable storage medium and executable by a processor to (1) receive domain related data and information, (2) test at least a portion of the received domain related data and information against a plurality of the applicability conditions to determine whether any of the applicability conditions are deemed applicable to the received domain related data and information, (3) identify one or more angle data structures for the narrative story based at least in part on which one or more angle data structures are associated with the applicability conditions deemed applicable to the received domain related data and information, and (4) render the narrative story using the identified one or more angle data structures.

1. A computer program product for automatically generating a narrative story using data and information, the computer program product comprising: a plurality of angle data structures that are readable by a processor and resident on a non-transitory computer-readable storage medium, each of the plurality of angle data structures comprising a plurality of data representations that connect a plurality of events, circumstances, and/or entities as a model of a thematic nature and not comprising specific text for a narrative story, each angle data structure being associated with at least one of a plurality of applicability conditions; and a plurality of instructions that are resident on a non-transitory computer-readable storage medium and executable by a processor to (1) receive domain related data and information, (2) test at least a portion of the received domain related data and information against a plurality of the applicability conditions to determine whether any of the applicability conditions are deemed applicable to the received domain related data and information, (3) identify one or more angle data structures for the narrative story based at least in part on which one or more angle data structures are associated with the applicability conditions deemed applicable to the received domain related data and information, and (4) render the narrative story using the identified one or more angle data structures. 19. The computer program product as recited in claim 1 , wherein the domain related data and information is representative of at least one member of the group consisting of (1) an actual event, (2) an actual circumstance, and (3) an actual entity, and wherein the instructions are further configured to automatically render a narrative story that is descriptive of the at least one member as influenced by the one or more thematic natures of the identified one or more angle data structures.

1. A computing system configured to facilitate enhanced instant messaging, the computing system comprising a processor and a memory, wherein the computing system includes a messaging module and a widget player module that, when executed by the processor, are configured to facilitate: creation of text for an instant message; selection of at least one widget from a plurality of customizable widgets; personalization of the at least one widget selected; embedding of the at least one widget within the instant message; rendering, by the processor in the computing system, the instant message containing the embedded at least one widget, the at least one widget being minimized and represented as a first icon using a minimized mode of the widget player module, the first icon displaying real-time retrieved and rendered information; and sending, by the processor in the computing system, the instant message to a remote computer, the remote computer comprising a widget adapter that enables a full view of the minimized at least one widget represented as the first icon in the instant message, the full view comprising additional information not shown in the first icon, the additional information comprising a reply request from the remote computer and an option for sending the reply to the computing system, wherein the reply is within a customized widget embedded in a return instant message, wherein the customized widget is represented as a second icon in the return instant message, the return instant message comprising text in response to the instant message, the first icon and the second icon, the second icon displaying the reply.

1. A computing system configured to facilitate enhanced instant messaging, the computing system comprising a processor and a memory, wherein the computing system includes a messaging module and a widget player module that, when executed by the processor, are configured to facilitate: creation of text for an instant message; selection of at least one widget from a plurality of customizable widgets; personalization of the at least one widget selected; embedding of the at least one widget within the instant message; rendering, by the processor in the computing system, the instant message containing the embedded at least one widget, the at least one widget being minimized and represented as a first icon using a minimized mode of the widget player module, the first icon displaying real-time retrieved and rendered information; and sending, by the processor in the computing system, the instant message to a remote computer, the remote computer comprising a widget adapter that enables a full view of the minimized at least one widget represented as the first icon in the instant message, the full view comprising additional information not shown in the first icon, the additional information comprising a reply request from the remote computer and an option for sending the reply to the computing system, wherein the reply is within a customized widget embedded in a return instant message, wherein the customized widget is represented as a second icon in the return instant message, the return instant message comprising text in response to the instant message, the first icon and the second icon, the second icon displaying the reply. 5. The messaging module of claim 1 wherein at least one widget selected from a plurality of customizable widgets is stored in a widget container.

6. A system to facilitate user productivity in visualizing and reviewing a set of document search results, the system comprising: a processing device; and a non-transitory, processor-readable storage medium, the non-transitory, processor-readable storage medium comprising one or more programming instructions that, when executed, cause the processing device to: receive a query request comprising two or more search terms as a computer machine input, wherein each search term from the one or more search terms is assigned a graphical indicator, search a corpora of electronically stored content for a set of at least two documents relevant to the query request, score a set of paragraphs associated with the set of at least two documents, rank the set of paragraphs based on the scoring, and display at least one boxed abacus icon that indicates whether the two or more search terms in the query request are present in a subset of the set of paragraphs, wherein: the subset comprises a preset number of paragraphs receiving higher scores determined in the ranking step than a set of paragraphs not included in the subset, each paragraph in the subset is assigned to a vertical line in a set of vertical lines, and the at least one boxed abacus icon comprises a depiction of the graphical indicators on each vertical line corresponding to a presence of the search term in the paragraph.

6. A system to facilitate user productivity in visualizing and reviewing a set of document search results, the system comprising: a processing device; and a non-transitory, processor-readable storage medium, the non-transitory, processor-readable storage medium comprising one or more programming instructions that, when executed, cause the processing device to: receive a query request comprising two or more search terms as a computer machine input, wherein each search term from the one or more search terms is assigned a graphical indicator, search a corpora of electronically stored content for a set of at least two documents relevant to the query request, score a set of paragraphs associated with the set of at least two documents, rank the set of paragraphs based on the scoring, and display at least one boxed abacus icon that indicates whether the two or more search terms in the query request are present in a subset of the set of paragraphs, wherein: the subset comprises a preset number of paragraphs receiving higher scores determined in the ranking step than a set of paragraphs not included in the subset, each paragraph in the subset is assigned to a vertical line in a set of vertical lines, and the at least one boxed abacus icon comprises a depiction of the graphical indicators on each vertical line corresponding to a presence of the search term in the paragraph. 7. The system of claim 6 , wherein: the at least one boxed abacus icon comprises a set of tiles arranged in a plurality of rows; and each row in the set of tiles represents a surfaced paragraph based on the scoring of the set of paragraphs.

11. A system for enforcing application-layer policies to documents, each policy defining a rule and an action, comprising: a XML parser for parsing a XML document received as streaming XML data in a hierarchical structure to enable evaluation of an object in the XML document; a simple policies data structure for storing XPath queries that do not use wildcard “*” and descendent “//” expressions; a complex policies data structure for storing XPath queries that use wildcard “*” and descendent “//” expressions; means for simultaneously querying the simple and complex policies data structures to identify all policies corresponding to the object; and means for executing the actions defined by the policies corresponding to the object identified in the data structures.

11. A system for enforcing application-layer policies to documents, each policy defining a rule and an action, comprising: a XML parser for parsing a XML document received as streaming XML data in a hierarchical structure to enable evaluation of an object in the XML document; a simple policies data structure for storing XPath queries that do not use wildcard “*” and descendent “//” expressions; a complex policies data structure for storing XPath queries that use wildcard “*” and descendent “//” expressions; means for simultaneously querying the simple and complex policies data structures to identify all policies corresponding to the object; and means for executing the actions defined by the policies corresponding to the object identified in the data structures. 13. The system of claim 11 , wherein querying the simple policies data structure is performed using a Deterministic Finite Automaton (DFA) structure, where two or more XPath queries that have a common prefix are merged.

1. A computer implemented method of converting at least some information of a composition of ontological subjects into at least one ordered array of data, said method comprises execution of a set of instructions, by one or more data processing or computing devices, configured to perform: partitioning, using one or more data processing or computing devices, the composition into one or more sets of partitions, wherein at least one of said one or more sets of partitions is assigned with a predefined ontological subject order l, wherein l is a variable and is represented by a string of one or more characters; identifying one or more sets of ontological subjects or partitions of the composition, wherein at least one set of said one or more sets of ontological subjects or partitions of the composition is assigned with a predefined ontological subject order k; wherein k is a variable and is represented by a string of one or more characters; constructing one or more data structure corresponding to at least one ordered array of data, wherein said at least one ordered array of data represents participation of some of said ontological subjects or partitions of the composition, assigned with the order k, into some of the partitions, assigned with the order l, by having a non-zero value in the corresponding entries of the at least one ordered array of data, wherein the ordered array of data represents a matrix, wherein each row of the matrix is representative of an ontological subjects or a partition of the composition, assigned with the order k, and each column of the matrix is representative of a partition from said set of partitions, assigned with order l, or vice versa; replacing one or more of ontological subjects or partitions of the composition, assigned with the order k, with an ontological subject, assigned with a predefined order, and updating respective entries of said one or more ontological subjects or partitions of the composition in the at least one ordered data array accordingly; and storing the one or more data structure corresponding to the at least one ordered array of data onto one or more non-transitory computer readable medium.

1. A computer implemented method of converting at least some information of a composition of ontological subjects into at least one ordered array of data, said method comprises execution of a set of instructions, by one or more data processing or computing devices, configured to perform: partitioning, using one or more data processing or computing devices, the composition into one or more sets of partitions, wherein at least one of said one or more sets of partitions is assigned with a predefined ontological subject order l, wherein l is a variable and is represented by a string of one or more characters; identifying one or more sets of ontological subjects or partitions of the composition, wherein at least one set of said one or more sets of ontological subjects or partitions of the composition is assigned with a predefined ontological subject order k; wherein k is a variable and is represented by a string of one or more characters; constructing one or more data structure corresponding to at least one ordered array of data, wherein said at least one ordered array of data represents participation of some of said ontological subjects or partitions of the composition, assigned with the order k, into some of the partitions, assigned with the order l, by having a non-zero value in the corresponding entries of the at least one ordered array of data, wherein the ordered array of data represents a matrix, wherein each row of the matrix is representative of an ontological subjects or a partition of the composition, assigned with the order k, and each column of the matrix is representative of a partition from said set of partitions, assigned with order l, or vice versa; replacing one or more of ontological subjects or partitions of the composition, assigned with the order k, with an ontological subject, assigned with a predefined order, and updating respective entries of said one or more ontological subjects or partitions of the composition in the at least one ordered data array accordingly; and storing the one or more data structure corresponding to the at least one ordered array of data onto one or more non-transitory computer readable medium. 8. The method of claim 1 wherein said one or more data arrays is used to calculate a semantic importance score or rank for an ontological subjects or a partition of the composition assigned with a predefined order.

7. A computer-implemented system comprising: at least one processor; at least one memory, wherein the at least one memory is coupled to the at least one processor; and one or more modules stored in the at least one memory and executed by the at least one processor, the one or more modules comprising instructions to: access a target member profile in an on-line social network system, using the at least one processor, the target member profile associated with a profile summary user interface (UI), the profile summary UI comprising a display area suitable for receiving user input; determine, using the at least one processor, from profiles in the on-line social network system, a set of member profiles using data from the target member profile, the set of member profiles and the target profile being a sub-network of profiles in the on-line social network system; extract a plurality of phrases from the sub-network of member profiles, using the at least one processor; calculate, for each pair of phrases w i and w j from the plurality of phrases, a correlation value as a sum of product values calculated with respect to each pair of profiles comprising a profile in the set of member profiles and the target profile, a product value calculated with respect to a pair comprising a k th profile in the set of member profiles and the target profile is a product of a value indicating similarity between the target member profile and the k th profile, a value indicating frequency of occurrence of the phrase w i in the k th profile, and a value indicating the frequency of occurrence of the phrase w j in the k th profile; and determine a rank for each phrase in the plurality of phrases, using the at least one processor, based on respective correlation values of pairs of phrases from the plurality of phrases, wherein the determining a rank for each phrase in the plurality of phrases comprises; constructing an affinity graph, nodes of the affinity graph representing respective phrases from the plurality of phrases and edges in the affinity graph representing correlation values for respective nodes attached to the edges, and applying a ranking algorithm to the affinity graph to determining a rank for each phrase in the plurality of phrases represented by the nodes in the affinity graph; select a predetermined number of top-ranking phrases from the plurality of phrases; and cause presentation of the selected phrases in a further display area of the profile summary UI.

7. A computer-implemented system comprising: at least one processor; at least one memory, wherein the at least one memory is coupled to the at least one processor; and one or more modules stored in the at least one memory and executed by the at least one processor, the one or more modules comprising instructions to: access a target member profile in an on-line social network system, using the at least one processor, the target member profile associated with a profile summary user interface (UI), the profile summary UI comprising a display area suitable for receiving user input; determine, using the at least one processor, from profiles in the on-line social network system, a set of member profiles using data from the target member profile, the set of member profiles and the target profile being a sub-network of profiles in the on-line social network system; extract a plurality of phrases from the sub-network of member profiles, using the at least one processor; calculate, for each pair of phrases w i and w j from the plurality of phrases, a correlation value as a sum of product values calculated with respect to each pair of profiles comprising a profile in the set of member profiles and the target profile, a product value calculated with respect to a pair comprising a k th profile in the set of member profiles and the target profile is a product of a value indicating similarity between the target member profile and the k th profile, a value indicating frequency of occurrence of the phrase w i in the k th profile, and a value indicating the frequency of occurrence of the phrase w j in the k th profile; and determine a rank for each phrase in the plurality of phrases, using the at least one processor, based on respective correlation values of pairs of phrases from the plurality of phrases, wherein the determining a rank for each phrase in the plurality of phrases comprises; constructing an affinity graph, nodes of the affinity graph representing respective phrases from the plurality of phrases and edges in the affinity graph representing correlation values for respective nodes attached to the edges, and applying a ranking algorithm to the affinity graph to determining a rank for each phrase in the plurality of phrases represented by the nodes in the affinity graph; select a predetermined number of top-ranking phrases from the plurality of phrases; and cause presentation of the selected phrases in a further display area of the profile summary UI. 11. The system of claim 7 , wherein the one or more modules are to recalculate, periodically, a rank for each phrase in the plurality of phrases.

1. A process for metasearching on the Internet, wherein the steps of the process are performed by a metasearch engine executing on a hardware device, the process comprising the steps of: (a) receiving a Hypertext Transfer Protocol request from a client device for the metasearch engine to send at least one stock related search query to a plurality of unique hosts that provide access to stock related information to be searched; (b) sending the at least one stock related search query to the plurality of unique hosts in response to the Hypertext Transfer Protocol request received from the client device; (c) receiving search results from the plurality of unique hosts in response to the at least one stock related search query sent to the plurality of unique hosts, wherein the search results comprise price data related to at least one stock; (d) incorporating the received search results comprising the price data related to the at least one stock and incorporating at least one related news item link into a response; (e) communicating the response from the metasearch engine to the client device.

1. A process for metasearching on the Internet, wherein the steps of the process are performed by a metasearch engine executing on a hardware device, the process comprising the steps of: (a) receiving a Hypertext Transfer Protocol request from a client device for the metasearch engine to send at least one stock related search query to a plurality of unique hosts that provide access to stock related information to be searched; (b) sending the at least one stock related search query to the plurality of unique hosts in response to the Hypertext Transfer Protocol request received from the client device; (c) receiving search results from the plurality of unique hosts in response to the at least one stock related search query sent to the plurality of unique hosts, wherein the search results comprise price data related to at least one stock; (d) incorporating the received search results comprising the price data related to the at least one stock and incorporating at least one related news item link into a response; (e) communicating the response from the metasearch engine to the client device. 2. The process for metasearching on the Internet of claim 1 , wherein step (e) further comprises: communicating at least one updated response from the metasearch engine to the client device.

14. A method comprising: receiving a feeling classification request from an application executing on a client device, feeling classification request comprising text data elements; in response, generating and transmitting a feeling classification response by: determining grammatical and semantic structure of the set of text data elements using a reverse sentence reconstruct (RSR) utility; generating sentence vectorization technique (SVT) models using a SVT utility; storing the SVT models, a labelled text corpus and a slang and spelling dictionary; generating a syntactic text tree with the text data elements using a parsing component of the RSR utility; and classifying feeling of the text data elements in the syntactic text tree using a classification component of the RSR utility.

14. A method comprising: receiving a feeling classification request from an application executing on a client device, feeling classification request comprising text data elements; in response, generating and transmitting a feeling classification response by: determining grammatical and semantic structure of the set of text data elements using a reverse sentence reconstruct (RSR) utility; generating sentence vectorization technique (SVT) models using a SVT utility; storing the SVT models, a labelled text corpus and a slang and spelling dictionary; generating a syntactic text tree with the text data elements using a parsing component of the RSR utility; and classifying feeling of the text data elements in the syntactic text tree using a classification component of the RSR utility. 18. The method of claim 14 , further comprising, as pre-training, obtaining the labelled text corpus, generating word vectors, generating phrase vectors, generating a parsing combination matrix, generating a parsing probability vector, and generating a part-of-speech matrix to output a randomized parsing SVT model.

2. A system for data correlation, having: a mobile terminal having a pick-up device and a user device, a correlation device which is spatially separate from the mobile terminal and is connected therewith by means of at least one network; the pick-up device having: an image acquisition element and a data record generator for generating at least one object data record from at least one acquired first image which represents a physical object, and an identification label, which uniquely determines an object-related acquisition procedure, and at least one information data record from at least one acquired second image, which represents coded information related to the physical object, and the identification label; the correlation device serving to extract the coded information from the information data record, for the semantic interpretation analysis of the extracted information in order to establish which parts of the extracted information have which semantic meaning, and to generate at least one combination data record from the results of the semantic analysis, the extracted information, and the at least one object data record having the same identification label as the extracted information data record; and the user device serving to store and further use the combination data record.

2. A system for data correlation, having: a mobile terminal having a pick-up device and a user device, a correlation device which is spatially separate from the mobile terminal and is connected therewith by means of at least one network; the pick-up device having: an image acquisition element and a data record generator for generating at least one object data record from at least one acquired first image which represents a physical object, and an identification label, which uniquely determines an object-related acquisition procedure, and at least one information data record from at least one acquired second image, which represents coded information related to the physical object, and the identification label; the correlation device serving to extract the coded information from the information data record, for the semantic interpretation analysis of the extracted information in order to establish which parts of the extracted information have which semantic meaning, and to generate at least one combination data record from the results of the semantic analysis, the extracted information, and the at least one object data record having the same identification label as the extracted information data record; and the user device serving to store and further use the combination data record. 18. The system according to claim 2 , wherein the user device is a personal computer of the user of the pick-up device.

1. A method of printing data, the method comprising: generating a first mark-up document that describes a file to be printed, the first mark-up document comprising text data and link data that indicates a location at which the file is stored in a storage unit; generating a second mark-up document that describes the file, the second mark-up document comprising identification information that identifies the file at the location indicated by the link data; generating a document to be transmitted to a printing device using the first mark-up document and the second mark-up document; transmitting the generated document to the printing device; analyzing the document at the printing device to obtain the link data and the identification information; transmitting a request for the file based on the extracted link data and identification information; extracting the file from the storage unit in response to the request for the file based on the extracted link data and identification information; receiving the file in response to the request for the file at the printing device; and outputting the file at the printing device.

1. A method of printing data, the method comprising: generating a first mark-up document that describes a file to be printed, the first mark-up document comprising text data and link data that indicates a location at which the file is stored in a storage unit; generating a second mark-up document that describes the file, the second mark-up document comprising identification information that identifies the file at the location indicated by the link data; generating a document to be transmitted to a printing device using the first mark-up document and the second mark-up document; transmitting the generated document to the printing device; analyzing the document at the printing device to obtain the link data and the identification information; transmitting a request for the file based on the extracted link data and identification information; extracting the file from the storage unit in response to the request for the file based on the extracted link data and identification information; receiving the file in response to the request for the file at the printing device; and outputting the file at the printing device. 6. The method according to claim 1 , wherein the outputting comprises simultaneously outputting the file and the text data at the printing device.

1. A computer-implemented method comprising: receiving, at a computer system, a collection of text-based terms associated with a document; performing, via the computer system, a statistical analysis on the text-based terms to identify a distribution of the text-based terms in the document, wherein the statistical analysis uses one or more locations at which the text-based terms appear in the document; and providing, via the computer system, representative terms for association with the document, wherein the representative terms are identified by identifying which of the text-based terms are most representative of the document based on the distribution of the text-based terms in the document.

1. A computer-implemented method comprising: receiving, at a computer system, a collection of text-based terms associated with a document; performing, via the computer system, a statistical analysis on the text-based terms to identify a distribution of the text-based terms in the document, wherein the statistical analysis uses one or more locations at which the text-based terms appear in the document; and providing, via the computer system, representative terms for association with the document, wherein the representative terms are identified by identifying which of the text-based terms are most representative of the document based on the distribution of the text-based terms in the document. 3. The method of claim 1 wherein performing the statistical analysis on the text-based terms to identify the distribution comprises: for a given text-based term of the text-based terms, detecting relative locations where the given text-based term can be found in the document; and based on the relative locations where the given text-based term can be found in the document, generating a weighted average location value specifying a centroid associated with occurrences of the given text-based term in the document.

1. A communications method, comprising: receiving a conversational speech input from a first user; converting the received speech input to a text representation thereof using an automated processor; communicating the text representation and speech input remotely from the first user to a second user in real time, as part of a conversation; and automatically reproducing the speech input and displaying the text representation in real time to the second user through an automated interface device, wherein the speech input is received by a speech application executing on the automated processor, the speech application further comprising a text dialog interface, further comprising the step of transferring the text representation to the text dialog interface and communicating the text representation by the speech application from the text dial; wherein the text dialog interface is in a separate application executing on the automated processor and the speech application transfers the text representation to said text dialog interface; and wherein the speech application receives the speech input as dictation and commands, and sends at least one command derived from the speech input to said separate application.

1. A communications method, comprising: receiving a conversational speech input from a first user; converting the received speech input to a text representation thereof using an automated processor; communicating the text representation and speech input remotely from the first user to a second user in real time, as part of a conversation; and automatically reproducing the speech input and displaying the text representation in real time to the second user through an automated interface device, wherein the speech input is received by a speech application executing on the automated processor, the speech application further comprising a text dialog interface, further comprising the step of transferring the text representation to the text dialog interface and communicating the text representation by the speech application from the text dial; wherein the text dialog interface is in a separate application executing on the automated processor and the speech application transfers the text representation to said text dialog interface; and wherein the speech application receives the speech input as dictation and commands, and sends at least one command derived from the speech input to said separate application. 6. The method according to claim 1 , wherein the second user is presented with both the reproduced speech input and the converted text representation communicated together as IP packets through the Internet.

1. A method for generating a word candidate to assist a user providing an input to a computing device, comprising: receiving, at the computing device, the input containing a plurality of words, wherein the computing device performs the operations of: determining a conditional count; determining an unconditional count; determining an adjustment factor fora pair of words of the plurality of words based on the unconditional count and the conditional count; generating a data structure defining a plurality of word clusters, individual word clusters of the plurality of word clusters include at least one word of the plurality of words; reconstructing the adjustment factor of the pair of words based on a number of common clusters between individual words of the pair of words; determining a candidate probability associated with the word candidate based, at least in part, on the reconstructed adjustment factor, wherein the word candidate is selected from individual words associated with the plurality of word clusters; generating an output containing the word candidate based, at least in part, on the candidate probability; and displaying the word candidate on a display screen of the computing device.

1. A method for generating a word candidate to assist a user providing an input to a computing device, comprising: receiving, at the computing device, the input containing a plurality of words, wherein the computing device performs the operations of: determining a conditional count; determining an unconditional count; determining an adjustment factor fora pair of words of the plurality of words based on the unconditional count and the conditional count; generating a data structure defining a plurality of word clusters, individual word clusters of the plurality of word clusters include at least one word of the plurality of words; reconstructing the adjustment factor of the pair of words based on a number of common clusters between individual words of the pair of words; determining a candidate probability associated with the word candidate based, at least in part, on the reconstructed adjustment factor, wherein the word candidate is selected from individual words associated with the plurality of word clusters; generating an output containing the word candidate based, at least in part, on the candidate probability; and displaying the word candidate on a display screen of the computing device. 6. The method of claim 1 , wherein reconstructing the adjustment factor of the pair of words is also based on a ranking of at least one correlation between the words.

24. The method of claim 23 , further comprising associating a trust level with the user based on the at least one suggestion keyword and the suggestion description.

24. The method of claim 23 , further comprising associating a trust level with the user based on the at least one suggestion keyword and the suggestion description. 25. The method of claim 24 , wherein the associating a trust level with the user comprises assigning a trust level to the user by other users.

8. A method comprising acts of: segmenting an unstructured text into a plurality of text sections; using at least one processor to identify a portion of text that fully or partially identifies a section heading for a first text section of the plurality of text sections; removing, from the first text section, the portion of text that fully or partially identifies the section heading; creating a structured text comprising the first text section and the section heading for the first text section, wherein the portion of text that fully or partially identifies the section heading has been removed from the first text section; and providing the structured text to a user.

8. A method comprising acts of: segmenting an unstructured text into a plurality of text sections; using at least one processor to identify a portion of text that fully or partially identifies a section heading for a first text section of the plurality of text sections; removing, from the first text section, the portion of text that fully or partially identifies the section heading; creating a structured text comprising the first text section and the section heading for the first text section, wherein the portion of text that fully or partially identifies the section heading has been removed from the first text section; and providing the structured text to a user. 11. The method of claim 8 , further comprising: receiving user input indicative of the user disapproving the section heading; and in response to the user input, re-inserting the portion of text that fully or partially identifies the section heading into the first text section.

1. A method comprising: receiving a query q from a particular user u who intends to find results that satisfy the query q with respect to a topic T u , the particular user u being characterized by user information θ u ; producing a generic topic distribution Pr r (T|q) associated with the query q that is germane to a population of generic users; producing a user-specific query-dependent topic distribution Pr(T u |q,θ u ) associated with the query q for the particular user u; generating personalized results for the particular user u based on the generic topic distribution Pr r (T|q) and the user-specific query-dependent topic distribution Pr(T u |q,θ u ); and forwarding the personalized results to the particular user u.

1. A method comprising: receiving a query q from a particular user u who intends to find results that satisfy the query q with respect to a topic T u , the particular user u being characterized by user information θ u ; producing a generic topic distribution Pr r (T|q) associated with the query q that is germane to a population of generic users; producing a user-specific query-dependent topic distribution Pr(T u |q,θ u ) associated with the query q for the particular user u; generating personalized results for the particular user u based on the generic topic distribution Pr r (T|q) and the user-specific query-dependent topic distribution Pr(T u |q,θ u ); and forwarding the personalized results to the particular user u. 3. The method of claim 1 , wherein said producing of the user-specific query-dependent topic distribution Pr(T u |q,θ u ) comprises: receiving a user-independent language model; receiving a user-specific query-independent distribution Pr(T u |θ u ) describing a probability that the particular user u is intent on finding identified topics; and computing the user-specific query-dependent topic distribution Pr(T u |q,θ u ) based on the user-independent language model and the user-specific query-independent distribution Pr(T u |θ u ), using Bayes' rule.

26. The computer-implemented method of claim 25 , further comprising assigning to the particular author profile one or more additional document groups, comprising: determining a correspondence score for each of one or more of the document groups in reference to the particular author profile; determining that the correspondence score for a first document group of the one or more document groups satisfies a threshold; and adding documents in the first document group to the particular author profile as documents authored by the particular author.

26. The computer-implemented method of claim 25 , further comprising assigning to the particular author profile one or more additional document groups, comprising: determining a correspondence score for each of one or more of the document groups in reference to the particular author profile; determining that the correspondence score for a first document group of the one or more document groups satisfies a threshold; and adding documents in the first document group to the particular author profile as documents authored by the particular author. 27. The computer-implemented method of claim 26 , wherein the correspondence score for each document group is based on a count of documents that are in both the document group and in the particular author profile, a count of documents that are in the document group and have never been in the particular author profile, and a count of documents that are in the document group and have been removed from the particular author profile.

12. A method, comprising: detecting a target with a first plurality of detectors, wherein the first plurality of detectors comprises a first appearance-based detector and a first silhouette-based detector; detecting the target with a second plurality of detectors, wherein the second plurality of detectors comprises a first appearance-based detector and a second silhouette-based detector; generating a first plurality of feature cues with the first plurality of detectors; generating a second plurality of feature cues with the second plurality of detectors; fusing the first plurality of feature cues and the second plurality of feature cues to create a set of target hypotheses; tracking the target based on the set of target hypotheses and a scene context analysis; and updating the tracking of the target based on a motion model; wherein fusing the plurality of feature cues to form a set of target hypotheses comprises fusing a first output from a first detector with a second output from a second detector, wherein the first and second outputs are of different types and the first detector operates on the output of the second detector; and wherein the tracking compensates for a motion of a radiation source tracked by a radiation imaging device.

12. A method, comprising: detecting a target with a first plurality of detectors, wherein the first plurality of detectors comprises a first appearance-based detector and a first silhouette-based detector; detecting the target with a second plurality of detectors, wherein the second plurality of detectors comprises a first appearance-based detector and a second silhouette-based detector; generating a first plurality of feature cues with the first plurality of detectors; generating a second plurality of feature cues with the second plurality of detectors; fusing the first plurality of feature cues and the second plurality of feature cues to create a set of target hypotheses; tracking the target based on the set of target hypotheses and a scene context analysis; and updating the tracking of the target based on a motion model; wherein fusing the plurality of feature cues to form a set of target hypotheses comprises fusing a first output from a first detector with a second output from a second detector, wherein the first and second outputs are of different types and the first detector operates on the output of the second detector; and wherein the tracking compensates for a motion of a radiation source tracked by a radiation imaging device. 16. The method of claim 12 , wherein the scene context analysis comprises monitoring a set of polygons representative of an environment where the target may exist.

1. An information processing apparatus comprising: a controller device comprising a data processing device; an input unit configured to input a plurality of new search keys, each new search key to be registered with a file for subsequent retrieval of the file based on a comparison involving the respective new search key, when registered, and a subsequently input search key that is input after registration of the respective new search key; a user information obtaining unit configured to, for each of the plurality of new search keys, obtain user information for identifying a user to be associated with the respective new search key; a group information obtaining unit configured to, for each of the plurality of new search keys, obtain group information for identifying a user group to be associated with the respective new search key; an addition unit configured to, for each of the plurality of new search keys, (a) form a composite search keyword from the respective new search key and the user information obtained by the user information obtaining unit or the group information obtained by the group information obtaining unit, and (b) add the composite search keyword to index information associated with the file for the subsequent retrieval of the file; a selection unit configured to select, for each of the plurality of new search keys and for the forming and adding performed by the addition unit, either the user information or the group information; and a registration unit configured to cause the index information, having the composite search keywords formed by the addition unit and added thereto by the addition unit according to the selection by the selection unit, to be registered with the file in a storage unit for the subsequent retrieval of the file, wherein each of the units is implemented at least in part by the controller device.

1. An information processing apparatus comprising: a controller device comprising a data processing device; an input unit configured to input a plurality of new search keys, each new search key to be registered with a file for subsequent retrieval of the file based on a comparison involving the respective new search key, when registered, and a subsequently input search key that is input after registration of the respective new search key; a user information obtaining unit configured to, for each of the plurality of new search keys, obtain user information for identifying a user to be associated with the respective new search key; a group information obtaining unit configured to, for each of the plurality of new search keys, obtain group information for identifying a user group to be associated with the respective new search key; an addition unit configured to, for each of the plurality of new search keys, (a) form a composite search keyword from the respective new search key and the user information obtained by the user information obtaining unit or the group information obtained by the group information obtaining unit, and (b) add the composite search keyword to index information associated with the file for the subsequent retrieval of the file; a selection unit configured to select, for each of the plurality of new search keys and for the forming and adding performed by the addition unit, either the user information or the group information; and a registration unit configured to cause the index information, having the composite search keywords formed by the addition unit and added thereto by the addition unit according to the selection by the selection unit, to be registered with the file in a storage unit for the subsequent retrieval of the file, wherein each of the units is implemented at least in part by the controller device. 4. An information processing apparatus as claimed in claim 1 , further comprising an encoding unit configured to, for each of at least some of the new search keys, cause the respective composite search keyword to be in an encoded form for the registration performed by the registration unit.

17. A computer-readable storage device containing instructions that when executed cause one or more processors to: identify a pool of features from multiple types of input, the pool of features comprising a first video feature from a video input, the first video feature comprising a parent rectangle within a feature rectangle in an image of the video input; calculate a numeric value associated with the first video feature by summing values of pixels in the parent rectangle; and generate a classifier for speaker detection using a learning algorithm wherein nodes of the classifier are selected using the pool of features, based on the numeric value associated with the first video feature.

17. A computer-readable storage device containing instructions that when executed cause one or more processors to: identify a pool of features from multiple types of input, the pool of features comprising a first video feature from a video input, the first video feature comprising a parent rectangle within a feature rectangle in an image of the video input; calculate a numeric value associated with the first video feature by summing values of pixels in the parent rectangle; and generate a classifier for speaker detection using a learning algorithm wherein nodes of the classifier are selected using the pool of features, based on the numeric value associated with the first video feature. 20. The computer-readable storage device of claim 17 , containing instructions that when executed cause one or more processors to define a second video feature within a short-term difference image or a long-term difference image calculated based on the image of the video input.

15. A search engine system, comprising: one or more processors; and one or more memory mediums couples to the one or more processors, wherein the one or more memory mediums store program instructions for modifying a search engine, wherein the program instructions are executable by the one or more processors to: create a search engine for a website, wherein said creating the search engine comprises creating search information for a plurality of webpages of the website, wherein the search information specifies a first set of information for each webpage; provide a search-customization user interface for modifying a ranking function of the search engine for the website, wherein the search-customization user interface specifies one or more first ranking factors; after the user inserts one or more first custom tags into source code of at least one webpage of the website, update the search information to include information related to the one or more custom tags, wherein the one or more custom tags are dedicated for customizing the search engine of the website; provide the search-customization user interface for modifying the ranking function of the search engine for the web site, wherein, in addition to the one or more first ranking factors, the search-customization user interface specifies one or more additional ranking factors corresponding to the one or more first custom tags, wherein each additional ranking factor is added to the search-customization user interface for a respective first custom tag inserted into the source code; receive first user input from the user, wherein the first user input specifies modifying a relative weight of a first additional ranking factor of the one or more additional ranking factors; automatically modify the ranking function of the search engine to use the relative weight of the first additional ranking factor based on the first user input; after automatically modifying the ranking function of the search engine to use the relative weight of the first additional ranking factor, receive a search query for the website; and provide a plurality of search results using the ranking function.

15. A search engine system, comprising: one or more processors; and one or more memory mediums couples to the one or more processors, wherein the one or more memory mediums store program instructions for modifying a search engine, wherein the program instructions are executable by the one or more processors to: create a search engine for a website, wherein said creating the search engine comprises creating search information for a plurality of webpages of the website, wherein the search information specifies a first set of information for each webpage; provide a search-customization user interface for modifying a ranking function of the search engine for the website, wherein the search-customization user interface specifies one or more first ranking factors; after the user inserts one or more first custom tags into source code of at least one webpage of the website, update the search information to include information related to the one or more custom tags, wherein the one or more custom tags are dedicated for customizing the search engine of the website; provide the search-customization user interface for modifying the ranking function of the search engine for the web site, wherein, in addition to the one or more first ranking factors, the search-customization user interface specifies one or more additional ranking factors corresponding to the one or more first custom tags, wherein each additional ranking factor is added to the search-customization user interface for a respective first custom tag inserted into the source code; receive first user input from the user, wherein the first user input specifies modifying a relative weight of a first additional ranking factor of the one or more additional ranking factors; automatically modify the ranking function of the search engine to use the relative weight of the first additional ranking factor based on the first user input; after automatically modifying the ranking function of the search engine to use the relative weight of the first additional ranking factor, receive a search query for the website; and provide a plurality of search results using the ranking function. 17. The search engine system of claim 15 , wherein the program instructions are further executable to: receive further user input from the user for a second ranking function, wherein the ranking functions are usable for a plurality of different search contexts.

1. An automated aircraft intent generation method based on specifications represented in formal languages, comprising: a) in a preprocessing step, calculating a set of motion primitives associated with a first aircraft intent description and a position of an aircraft based on combinations of AIDL instructions, the motion primitives including steady-state conditions or maneuvers to bring an aircraft from one steady-state condition to another; b) representing the motion primitives in AIDL; c) collecting information associated with at least one of 1) an aircraft performance model, 2) an environmental model, 3) a flight dynamic model, or 4) the motion primitives represented in AIDL; d) initializing a finite state machine based on the collected information, the finite state machine being configured to concatenate motion primitives, wherein states of the finite state machine correspond to steady-state conditions and transitions between the states are defined by maneuvers; e) collecting specifications associated with at least one of: 1) flight plan instructions, 2) user preference indications, or 3) operational context indications; f) representing the specifications in a first formal language; g) combining the initialized finite state machine with the specifications represented in the first formal language to obtain a trajectory that satisfies a trajectory specification threshold; h) determining whether the obtained trajectory satisfies the trajectory specification threshold; i) in response to determining that the obtained trajectory does not satisfy the trajectory specification threshold, iteratively initializing the finite state machine based on the information until a subsequently determined set of motion primitives satisfies the trajectory specification threshold, the subsequently determined set of motion primitives determined using incrementally modified motion primitives; and j) in response to determining that the obtained trajectory satisfies the trajectory specification threshold, producing a representation of a second aircraft intent description associated with the obtained trajectory represented in AIDL and finalizing the method.

1. An automated aircraft intent generation method based on specifications represented in formal languages, comprising: a) in a preprocessing step, calculating a set of motion primitives associated with a first aircraft intent description and a position of an aircraft based on combinations of AIDL instructions, the motion primitives including steady-state conditions or maneuvers to bring an aircraft from one steady-state condition to another; b) representing the motion primitives in AIDL; c) collecting information associated with at least one of 1) an aircraft performance model, 2) an environmental model, 3) a flight dynamic model, or 4) the motion primitives represented in AIDL; d) initializing a finite state machine based on the collected information, the finite state machine being configured to concatenate motion primitives, wherein states of the finite state machine correspond to steady-state conditions and transitions between the states are defined by maneuvers; e) collecting specifications associated with at least one of: 1) flight plan instructions, 2) user preference indications, or 3) operational context indications; f) representing the specifications in a first formal language; g) combining the initialized finite state machine with the specifications represented in the first formal language to obtain a trajectory that satisfies a trajectory specification threshold; h) determining whether the obtained trajectory satisfies the trajectory specification threshold; i) in response to determining that the obtained trajectory does not satisfy the trajectory specification threshold, iteratively initializing the finite state machine based on the information until a subsequently determined set of motion primitives satisfies the trajectory specification threshold, the subsequently determined set of motion primitives determined using incrementally modified motion primitives; and j) in response to determining that the obtained trajectory satisfies the trajectory specification threshold, producing a representation of a second aircraft intent description associated with the obtained trajectory represented in AIDL and finalizing the method. 7. The method of claim 1 , further including updating at least one of the information and the specifications without precomputed motion planning.

18. An apparatus that is configured to transform an input data stream comprising data that is expressed in a non-linguistic format into a format that can be expressed linguistically in a textual output, the apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to at least: access a document plan containing one or more messages, wherein messages represent a phrase or a simple sentence and are created in an instance in which the input data stream comprises data that satisfies one or more message requirements; generate a text specification containing one or more phrase specifications that correspond to the one or more messages in the document plan; apply a set of lexicalization rules to each of the one or more messages to populate the one or more phrase specifications, wherein the set of lexicalization rules are specified using a microplanning rule specification language that is configured to hide linguistic complexities from a user and comprise a set of message-level rules and a set of slot-level rules; and realize the text specification to generate a textual output that linguistically describes at least a portion of the input data stream, wherein the textual output is displayable via a user interface.

18. An apparatus that is configured to transform an input data stream comprising data that is expressed in a non-linguistic format into a format that can be expressed linguistically in a textual output, the apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to at least: access a document plan containing one or more messages, wherein messages represent a phrase or a simple sentence and are created in an instance in which the input data stream comprises data that satisfies one or more message requirements; generate a text specification containing one or more phrase specifications that correspond to the one or more messages in the document plan; apply a set of lexicalization rules to each of the one or more messages to populate the one or more phrase specifications, wherein the set of lexicalization rules are specified using a microplanning rule specification language that is configured to hide linguistic complexities from a user and comprise a set of message-level rules and a set of slot-level rules; and realize the text specification to generate a textual output that linguistically describes at least a portion of the input data stream, wherein the textual output is displayable via a user interface. 29. The apparatus of claim 18 , wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the apparatus to at least apply the set of lexicalization rules by being further configured to: identify one or more correspondences between a structure of the one or more messages and one or more natural language syntactic constituents that are used to express a linguistic output and a structure of the one or more messages.

1. A search apparatus comprising: a single search interface configured for display on a display device, the single search interface comprising a plurality of user selectable search mode interfaces, including at least: an email search mode interface having email-specific attribute search fields including at least a date field, a from field, and a sender field; a file search mode interface having file-specific attribute search fields including at least a file name field, a file type field, a date field, a file size field, and a path field; a favorites search mode interface used to search Web pages designated by a user as a favorite Web page; a Web history search mode interface having a date field, a title field, a size field, and a search term field; an email attachment search mode interface having a name field, a date field, a size field, and an extension field; wherein each of the email search mode interface, the file search mode interface, the favorites search mode interface, the Web history search mode interface, and the email attachment search mode interface further comprises a list pane, used to display a list of search result items, and a view pane, used to display the contents of a selected search result item; an index module configured to generate: an email index; a file index; a Web page index; one or more memories configured to store the email index, the file index and the Web page index; and a search module configured to perform incremental searching of at least one of the email index, the file index, and the Web page index in response to the user entering characters into at least one search mode interface field.

1. A search apparatus comprising: a single search interface configured for display on a display device, the single search interface comprising a plurality of user selectable search mode interfaces, including at least: an email search mode interface having email-specific attribute search fields including at least a date field, a from field, and a sender field; a file search mode interface having file-specific attribute search fields including at least a file name field, a file type field, a date field, a file size field, and a path field; a favorites search mode interface used to search Web pages designated by a user as a favorite Web page; a Web history search mode interface having a date field, a title field, a size field, and a search term field; an email attachment search mode interface having a name field, a date field, a size field, and an extension field; wherein each of the email search mode interface, the file search mode interface, the favorites search mode interface, the Web history search mode interface, and the email attachment search mode interface further comprises a list pane, used to display a list of search result items, and a view pane, used to display the contents of a selected search result item; an index module configured to generate: an email index; a file index; a Web page index; one or more memories configured to store the email index, the file index and the Web page index; and a search module configured to perform incremental searching of at least one of the email index, the file index, and the Web page index in response to the user entering characters into at least one search mode interface field. 4. The search apparatus as defined in claim 1 , further comprising an auto-hide control bar including a plurality of search mode interface tabs that displayed in response to a user moving a cursor to a first area.

5. The method of claim 4 , wherein the different user types include at least one of a content provider, a content retriever, and a content evaluator.

5. The method of claim 4 , wherein the different user types include at least one of a content provider, a content retriever, and a content evaluator. 6. The method of claim 5 , wherein a first tag chain specified by the content provider is assigned a higher metric than a a second tag chain specified by the content retriever.

1. A computerized method comprising: receiving multiple documents from at least one machine-readable media, the multiple documents having a hierarchical relationship relative to each other, the multiple documents including a first document and a second document, the hierarchical relationship between the first document and the second document being such that the second document is the child of the first document in the hierarchy and is associated with a specific subsection of the first document and the second document includes a replacement or addition for the specific subsection of the first document and an entirety of the second document is included in the first document as an enhancement to the first document; and simultaneously displaying the multiple documents on a display screen such that the multiple documents are distinct and are arranged according to the hierarchical relationship, wherein the position at which the second document is displayed is entirely within the first document and that the position of the second document within the first document is based on the specific subsection of the first document that the second document is associated with.

1. A computerized method comprising: receiving multiple documents from at least one machine-readable media, the multiple documents having a hierarchical relationship relative to each other, the multiple documents including a first document and a second document, the hierarchical relationship between the first document and the second document being such that the second document is the child of the first document in the hierarchy and is associated with a specific subsection of the first document and the second document includes a replacement or addition for the specific subsection of the first document and an entirety of the second document is included in the first document as an enhancement to the first document; and simultaneously displaying the multiple documents on a display screen such that the multiple documents are distinct and are arranged according to the hierarchical relationship, wherein the position at which the second document is displayed is entirely within the first document and that the position of the second document within the first document is based on the specific subsection of the first document that the second document is associated with. 5. The computerized method of claim 1 , wherein a third document of the multiple documents is a different enhancement to the second document, wherein the different enhancement is at least one of a replacement of part of the second document or an addition to the second document.

20. A method for project selection, comprising: generating a list of enterprise development projects to be funded using input from an input graphical user interface, the input from the input graphical user interface defines a plurality of attributes of each of the enterprise development projects to be funded, wherein one of the plurality of attributes links each of the enterprise development projects with a corresponding one of a plurality of enterprise strategic initiatives with which the enterprise development project is aligned; maintaining the enterprise development projects to be funded in a database; assigning a priority to each of the enterprise development projects to be funded; displaying the enterprise development projects to be funded in a priority graphical user interface with the enterprise development projects ordered according to the priorities assigned to the enterprise development projects; scoring the enterprise development projects to be funded to calculate a quality of each of the enterprise development projects to be funded, wherein the quality of each of the enterprise development projects to be funded are scored based on a the plurality of attributes of each of the enterprise development projects using an algorithm, wherein the algorithm changes in response to changes to the enterprise strategic initiatives, and the algorithm is stored in the database; displaying the enterprise development projects to be funded in a quality graphical user interface with the enterprise development projects ordered according to the quality level calculated for each of the enterprise development projects, grouping, by a mapping component, the enterprise development projects to be funded according to the strategic initiatives associated with the enterprise development projects to be funded; displaying, by the mapping component, the enterprise development projects to be funded in a mapping graphical user interface with each of the enterprise development projects being displayed with the quality and the priority of the enterprise development projects, and wherein the mapping component displays the enterprise development projects to be funded in groups according to the corresponding strategic initiatives linked with the enterprise development projects to be funded; verifying that the enterprise development projects to be funded are consistent with considerations other than the scoring of the enterprise development projects, wherein the considerations other than the scoring include a change mandated by one or more of a new legislation, a changed regulation, or a court order; and selecting to fund one or more of the enterprise development projects to be funded based upon an analysis of one or more of the priority graphical user interface, the quality graphical user interface, and the mapping graphical user interface.

20. A method for project selection, comprising: generating a list of enterprise development projects to be funded using input from an input graphical user interface, the input from the input graphical user interface defines a plurality of attributes of each of the enterprise development projects to be funded, wherein one of the plurality of attributes links each of the enterprise development projects with a corresponding one of a plurality of enterprise strategic initiatives with which the enterprise development project is aligned; maintaining the enterprise development projects to be funded in a database; assigning a priority to each of the enterprise development projects to be funded; displaying the enterprise development projects to be funded in a priority graphical user interface with the enterprise development projects ordered according to the priorities assigned to the enterprise development projects; scoring the enterprise development projects to be funded to calculate a quality of each of the enterprise development projects to be funded, wherein the quality of each of the enterprise development projects to be funded are scored based on a the plurality of attributes of each of the enterprise development projects using an algorithm, wherein the algorithm changes in response to changes to the enterprise strategic initiatives, and the algorithm is stored in the database; displaying the enterprise development projects to be funded in a quality graphical user interface with the enterprise development projects ordered according to the quality level calculated for each of the enterprise development projects, grouping, by a mapping component, the enterprise development projects to be funded according to the strategic initiatives associated with the enterprise development projects to be funded; displaying, by the mapping component, the enterprise development projects to be funded in a mapping graphical user interface with each of the enterprise development projects being displayed with the quality and the priority of the enterprise development projects, and wherein the mapping component displays the enterprise development projects to be funded in groups according to the corresponding strategic initiatives linked with the enterprise development projects to be funded; verifying that the enterprise development projects to be funded are consistent with considerations other than the scoring of the enterprise development projects, wherein the considerations other than the scoring include a change mandated by one or more of a new legislation, a changed regulation, or a court order; and selecting to fund one or more of the enterprise development projects to be funded based upon an analysis of one or more of the priority graphical user interface, the quality graphical user interface, and the mapping graphical user interface. 21. The method of claim 20 wherein scoring the projects further includes: receiving a characterization of the plurality of attributes of each of the enterprise development projects; and calculating the score based on the characterization.

21. A program storage device readable by machine, tangibly embodying a program of instructions in the form of an internet browser application executable by machine to perform the method steps for enhancing search results comprising the steps of a. receiving a list of results from a third-party search engine in a first rank order, b. comparing the list of results to a list of websites in a database, wherein the database includes a rating value for each website in the database, c. determining the rating value for each result in the list of results which was found in the list of websites in the database, wherein the rating value is determined by evaluating a plurality of predetermined criteria wherein the plurality of predetermined criteria are weighed such that not all criteria contribute equally to the determination of the rating value, such weighing being established by an evaluator of the website and the same criteria weighing being applied to all websites evaluated by the evaluator who determined the rating value, d. assigning a null value to the rating value for each result in the list of results which was not found in the list of websites in the database, e. displaying the rating value for at least one result in the list of results in a toolbar of the browser while maintaining the first rank order that was established by the third-party search engine.

21. A program storage device readable by machine, tangibly embodying a program of instructions in the form of an internet browser application executable by machine to perform the method steps for enhancing search results comprising the steps of a. receiving a list of results from a third-party search engine in a first rank order, b. comparing the list of results to a list of websites in a database, wherein the database includes a rating value for each website in the database, c. determining the rating value for each result in the list of results which was found in the list of websites in the database, wherein the rating value is determined by evaluating a plurality of predetermined criteria wherein the plurality of predetermined criteria are weighed such that not all criteria contribute equally to the determination of the rating value, such weighing being established by an evaluator of the website and the same criteria weighing being applied to all websites evaluated by the evaluator who determined the rating value, d. assigning a null value to the rating value for each result in the list of results which was not found in the list of websites in the database, e. displaying the rating value for at least one result in the list of results in a toolbar of the browser while maintaining the first rank order that was established by the third-party search engine. 22. The program storage device as recited in claim 21 , wherein the method further comprises the step of displaying the list of related websites in the toolbar of the browser, ordered by the order value.

14. A non-transitory computer readable medium having stored thereon a data structure for protecting a document, the document being stored in a computer-controlled repository, the data structure comprising: data records defining each of, two or more management groups, one or more relationship link each associated between a pair of the management groups, and the document; wherein each management group record having a management group type and a management subtype; wherein each relationship record associates a first management group to a second management group for defining a hierarchy of an enterprise organisation, each relationship record includes a relationship link type selected from a predefined set including at least two relationship link types; wherein each document record has respective document properties, the document properties being indicative of an access restriction to the document, the document properties being indicative of a first management group having ownership of the document, the document properties being further indicative of an access restriction to the document for another management group on the basis of the hierarchy of the enterprise organisation being dependant on a relationship link type associating such management group to the first management group, wherein the document properties comprise a respective access restriction associated with each of the at least two relationship link types included in the predefined set; and wherein the document properties are reviewed and access rights including a level of access is associated to the respective document, the access rights stored with the document properties further granting access to a second management group in accordance with the hierarchy of the enterprise organisation and relationship link type associating the second management group through to the first management group; wherein access to a document is granted to an employee belonging to the second management group when at least the second management group satisfies respective access rights associated with the document.

14. A non-transitory computer readable medium having stored thereon a data structure for protecting a document, the document being stored in a computer-controlled repository, the data structure comprising: data records defining each of, two or more management groups, one or more relationship link each associated between a pair of the management groups, and the document; wherein each management group record having a management group type and a management subtype; wherein each relationship record associates a first management group to a second management group for defining a hierarchy of an enterprise organisation, each relationship record includes a relationship link type selected from a predefined set including at least two relationship link types; wherein each document record has respective document properties, the document properties being indicative of an access restriction to the document, the document properties being indicative of a first management group having ownership of the document, the document properties being further indicative of an access restriction to the document for another management group on the basis of the hierarchy of the enterprise organisation being dependant on a relationship link type associating such management group to the first management group, wherein the document properties comprise a respective access restriction associated with each of the at least two relationship link types included in the predefined set; and wherein the document properties are reviewed and access rights including a level of access is associated to the respective document, the access rights stored with the document properties further granting access to a second management group in accordance with the hierarchy of the enterprise organisation and relationship link type associating the second management group through to the first management group; wherein access to a document is granted to an employee belonging to the second management group when at least the second management group satisfies respective access rights associated with the document. 17. The non-transitory computer readable medium of claim 14 wherein: each relationship record has a type selected from the predefined set including at least two relationship link types selected from any two or more of: “reports to”, “services”, and “other”.

2. The method of claim 1 , wherein the graphical user interface tool includes a web browser.

2. The method of claim 1 , wherein the graphical user interface tool includes a web browser. 4. The method of claim 2 , wherein the graphical user interface tool further includes a plurality of predefined extraction patterns.

18. The semantic processor according to claim 17 , wherein the cause eSAO comprises one or more eSAO components and the effect eSAO comprises one or more eSAO components different than the one or more eSAO components of the cause eSAO.

18. The semantic processor according to claim 17 , wherein the cause eSAO comprises one or more eSAO components and the effect eSAO comprises one or more eSAO components different than the one or more eSAO components of the cause eSAO. 19. The semantic processor according to claim 18 , wherein the eSAO components of the cause eSAO and the effect eSAO each comprise text related to one or more elements of the group consisting of: subjects, objects, actions, adjectives, prepositions, indirect objects, and adverbs.

28. A method of querying arbitrarily structured records in a database, the method being implemented in a query engine, the query engine being executable by a processor operably connected to a memory device, and the method comprising the following steps executed in an order and repetition determined by the query engine: building, in a memory device, a query structure containing a selection criterion and information defining a query; retrieving indicia of records from the database in accordance with the query structure; evaluating each of the indicia according to the selection criterion to determine whether the indicia satisfy the selection criterion; and returning selected indicia that satisfy the selection criterion.

28. A method of querying arbitrarily structured records in a database, the method being implemented in a query engine, the query engine being executable by a processor operably connected to a memory device, and the method comprising the following steps executed in an order and repetition determined by the query engine: building, in a memory device, a query structure containing a selection criterion and information defining a query; retrieving indicia of records from the database in accordance with the query structure; evaluating each of the indicia according to the selection criterion to determine whether the indicia satisfy the selection criterion; and returning selected indicia that satisfy the selection criterion. 29. The method of claim 28, wherein the query structure represents a hybrid query.

1. A computer program product, embodied in a computer-readable medium, the computer program product being operable to cause data processing apparatus to: a) receive, during a first time duration, a first electronic document being communicated between business entities, the first electronic document comprising instances of a plurality of business data elements, the first electronic document having a format corresponding to a business communication schema, wherein the business communication schema includes a set of predefined business data elements for use in electronically communicating business data from a first business entity to a second business entity; b) identify an instance of a particular business data element in the first electronic document; c) increment a counter associated with the particular business data element in response to identifying the instance of the particular business data element in the first electronic document; d) receive, during the first time duration, a second electronic document being communicated between business entities; e) identify an instance of the particular business data element in the second electronic document; f) increment the counter associated with the particular business data element in response to identifying the instance of the particular business data element in the second electronic document; g) subsequent to elapse of the first time duration, compare the counter with a threshold value; h) based on the counter being less than the threshold value, delete the particular business data element from the business communication schema; i) prior to deleting the particular business data element from the business communication schema, notify a user if the counter is less than the threshold value; j) receive an instruction from the user to remove the particular business data element from the business communication schema: k) reset the counter when the counter is at least equal to the threshold value; l) begin a second time duration upon resetting of the counter; and m) repeat operations b) through h) for a third electronic document being communicated between business entities during the second time duration.

1. A computer program product, embodied in a computer-readable medium, the computer program product being operable to cause data processing apparatus to: a) receive, during a first time duration, a first electronic document being communicated between business entities, the first electronic document comprising instances of a plurality of business data elements, the first electronic document having a format corresponding to a business communication schema, wherein the business communication schema includes a set of predefined business data elements for use in electronically communicating business data from a first business entity to a second business entity; b) identify an instance of a particular business data element in the first electronic document; c) increment a counter associated with the particular business data element in response to identifying the instance of the particular business data element in the first electronic document; d) receive, during the first time duration, a second electronic document being communicated between business entities; e) identify an instance of the particular business data element in the second electronic document; f) increment the counter associated with the particular business data element in response to identifying the instance of the particular business data element in the second electronic document; g) subsequent to elapse of the first time duration, compare the counter with a threshold value; h) based on the counter being less than the threshold value, delete the particular business data element from the business communication schema; i) prior to deleting the particular business data element from the business communication schema, notify a user if the counter is less than the threshold value; j) receive an instruction from the user to remove the particular business data element from the business communication schema: k) reset the counter when the counter is at least equal to the threshold value; l) begin a second time duration upon resetting of the counter; and m) repeat operations b) through h) for a third electronic document being communicated between business entities during the second time duration. 3. The computer program product of claim 1 wherein the first electronic document is received from a translation module, the translation module operable to translate the first electronic document into the format corresponding to the business communication schema from a different communication schema format.

7. The computer program product of claim 6 , further comprising computer readable program code configured to detect syllable repetition via: aligning syllables; and comparing aligned syllables to detect repeated syllables.

7. The computer program product of claim 6 , further comprising computer readable program code configured to detect syllable repetition via: aligning syllables; and comparing aligned syllables to detect repeated syllables. 9. The computer program product of claim 7 , wherein comparing aligned syllables further comprises comparing at least two adjacent syllables using frame level features based on distance computation metrics.

10. A computer program product of a non-transitory computer readable medium usable with a programmable computer, the computer program product having computer-readable code embodied therein for optimizing mail sorting, the computer-readable code comprising instructions for: creating a postal code table in a sort control module, the postal code table comprising a sort scheme for each of a plurality of postal code ranges, the sort scheme including a pass number indicating during which of two passes documents within each postal code range will be passed through an envelope sorter; receiving an unsorted document print stream from a data source; creating a document parameter table in the sort control module, the parameter table comprising information for determining boundaries of each of a plurality of documents in the unsorted print stream to be printed and for determining a location of a postal code in each document; scanning the unsorted document print stream in the sort control module; in response to the scanned unsorted document print stream and the document parameter table, constructing an index table in the sort control module incorporating information from the postal code table, the index table indicating the location of each document in the unsorted document print stream and its scheme; reordering the index table in the sort control module according to the scheme of each document in the unsorted document print stream; generating a sorted document print stream as an output file in the sort control module in accordance with the reordered index table, whereby the documents are arranged in the sorted document print stream in order of their respective priorities; printing the documents in a printer from the sorted document print stream in the order determined in the index table and sending the printed documents to an inserter machine to be inserted into envelopes; separating the printed documents into either a pass 1 bin for documents having a sort scheme with a first pass number or a pass 2 bin for documents having a sort scheme with a second pass number; feeding the documents of the pass 1 bin into a sorting machine to sort the documents for deposit into first mailing trays to be presented to the post office; and upon completion of the sorting of the pass 1 bin, feeding the documents of the pass 2 bin into a sorting machine to sort the documents for deposit into second mailing trays to be presented to the post office.

10. A computer program product of a non-transitory computer readable medium usable with a programmable computer, the computer program product having computer-readable code embodied therein for optimizing mail sorting, the computer-readable code comprising instructions for: creating a postal code table in a sort control module, the postal code table comprising a sort scheme for each of a plurality of postal code ranges, the sort scheme including a pass number indicating during which of two passes documents within each postal code range will be passed through an envelope sorter; receiving an unsorted document print stream from a data source; creating a document parameter table in the sort control module, the parameter table comprising information for determining boundaries of each of a plurality of documents in the unsorted print stream to be printed and for determining a location of a postal code in each document; scanning the unsorted document print stream in the sort control module; in response to the scanned unsorted document print stream and the document parameter table, constructing an index table in the sort control module incorporating information from the postal code table, the index table indicating the location of each document in the unsorted document print stream and its scheme; reordering the index table in the sort control module according to the scheme of each document in the unsorted document print stream; generating a sorted document print stream as an output file in the sort control module in accordance with the reordered index table, whereby the documents are arranged in the sorted document print stream in order of their respective priorities; printing the documents in a printer from the sorted document print stream in the order determined in the index table and sending the printed documents to an inserter machine to be inserted into envelopes; separating the printed documents into either a pass 1 bin for documents having a sort scheme with a first pass number or a pass 2 bin for documents having a sort scheme with a second pass number; feeding the documents of the pass 1 bin into a sorting machine to sort the documents for deposit into first mailing trays to be presented to the post office; and upon completion of the sorting of the pass 1 bin, feeding the documents of the pass 2 bin into a sorting machine to sort the documents for deposit into second mailing trays to be presented to the post office. 11. The computer program product of claim 10 , wherein the instructions for creating a postal code table comprise instructions for generating a sorter number for each of the plurality of postal code ranges.

33. A method as recited in claim 32 wherein said guidance command includes the summation of the selected control output membership functions by a centroid weighted method.

33. A method as recited in claim 32 wherein said guidance command includes the summation of the selected control output membership functions by a centroid weighted method. 34. A method as recited in claim 33 wherein each selected sensed variable membership function produces a scaling factor in response to the magnitude of the corresponding sensed variable signal and wherein each selected control output membership function is scaled by an amount corresponding to the minimum scaling factor value of the corresponding sensed linguistic variable signal producing the control output membership function.

1. A computer-implemented method comprising: selecting a process integration (PI) scenario definition and a desired party with which to establish communication using an interface; determining at least one semantic contract associated with the selected PI scenario definition, wherein the at least one semantic contract specifies one or more related message interfaces with particular operations and signatures that are provided or consumed in an interaction between computing parties along with additional constraints, wherein the interaction is described by means of a set of operations, clustered into interfaces with a single shared interface definition between the computing parties, wherein the definition introduces detailed constraints requiring an explicit definition of valid uses of the interaction, and wherein, for each particular interaction, one definition of the interaction is established as binding on each computing party and forms a basis for a single data model of an interface for the particular interaction; querying the desired party to determine familiarity with the selected PI scenario definition and the at least one semantic contract; analyzing known contract definitions with contract usages for each determined semantic contract to generate first party analysis results; computing an intersection between the first party analysis results and corresponding received analysis results received from the desired party; determining an agreed upon set of processing types with the desired party by performing an intersection with contract usage processing types exchanged with the desired party; and generating a technical specification for a message signature based upon the agreed upon set of processing types.

1. A computer-implemented method comprising: selecting a process integration (PI) scenario definition and a desired party with which to establish communication using an interface; determining at least one semantic contract associated with the selected PI scenario definition, wherein the at least one semantic contract specifies one or more related message interfaces with particular operations and signatures that are provided or consumed in an interaction between computing parties along with additional constraints, wherein the interaction is described by means of a set of operations, clustered into interfaces with a single shared interface definition between the computing parties, wherein the definition introduces detailed constraints requiring an explicit definition of valid uses of the interaction, and wherein, for each particular interaction, one definition of the interaction is established as binding on each computing party and forms a basis for a single data model of an interface for the particular interaction; querying the desired party to determine familiarity with the selected PI scenario definition and the at least one semantic contract; analyzing known contract definitions with contract usages for each determined semantic contract to generate first party analysis results; computing an intersection between the first party analysis results and corresponding received analysis results received from the desired party; determining an agreed upon set of processing types with the desired party by performing an intersection with contract usage processing types exchanged with the desired party; and generating a technical specification for a message signature based upon the agreed upon set of processing types. 6. The method of claim 1 , further comprising implementing the interface compliant with the generated technical specification.

12. The computer-implemented method of claim 11 , wherein selecting, from each list, a second translation and adding the second translation to the second text corpus includes each selected second translation being selected based on a measure of absence of matching words between the second translation and one or more corresponding selected translations from the second text corpus.

12. The computer-implemented method of claim 11 , wherein selecting, from each list, a second translation and adding the second translation to the second text corpus includes each selected second translation being selected based on a measure of absence of matching words between the second translation and one or more corresponding selected translations from the second text corpus. 13. The computer-implemented method of claim 12 , wherein selecting, from each list, a second translation and adding the second translation to the second text corpus includes selecting the second translation from within a subset of translations of each list of translations, the subset determined based on the machine translation accuracy scores.

23. A method, according to claim 22, further comprising selecting the portion of the product knowledge in a manner established by the data model and the user-defined relationship.

23. A method, according to claim 22, further comprising selecting the portion of the product knowledge in a manner established by the data model and the user-defined relationship. 24. A method, according to claim 23, wherein the data instance contains attribute objects for use in selecting the portion of the product knowledge for presentation to the user.

1. A computer-implemented method for tracking one or more catheter objects in a sequence of images, the method comprising: determining, by a computer, a foreground portion of the first image comprising portions of the first image corresponding to one or more catheter electrode locations; determining, by the computer, a background portion of the first image which excludes the foreground portion; applying, by the computer, a steerable filter or a pre-processing method to the background portion of the first image to create a non-catheter structures mask which excludes ridge-like structures in the background portion of the first image; generating, by the computer, a dictionary based on catheter object locations in the first image, wherein sparse coding is used to represent the non-catheter structures mask as a plurality of basis vectors in the dictionary; identifying, by the computer, one or more catheter object landmark candidates in the sequence of images; generating, by the computer, a plurality of tracking hypothesis for the catheter object landmark candidates; generating, by the computer, a voting score for the catheter object landmark candidates based on a voting contribution of each of a plurality of image patches used to localize the catheter object locations in the first image; and selecting, by the computer, a first tracking hypothesis from the plurality of tracking hypothesis based on the dictionary and the voting score.

1. A computer-implemented method for tracking one or more catheter objects in a sequence of images, the method comprising: determining, by a computer, a foreground portion of the first image comprising portions of the first image corresponding to one or more catheter electrode locations; determining, by the computer, a background portion of the first image which excludes the foreground portion; applying, by the computer, a steerable filter or a pre-processing method to the background portion of the first image to create a non-catheter structures mask which excludes ridge-like structures in the background portion of the first image; generating, by the computer, a dictionary based on catheter object locations in the first image, wherein sparse coding is used to represent the non-catheter structures mask as a plurality of basis vectors in the dictionary; identifying, by the computer, one or more catheter object landmark candidates in the sequence of images; generating, by the computer, a plurality of tracking hypothesis for the catheter object landmark candidates; generating, by the computer, a voting score for the catheter object landmark candidates based on a voting contribution of each of a plurality of image patches used to localize the catheter object locations in the first image; and selecting, by the computer, a first tracking hypothesis from the plurality of tracking hypothesis based on the dictionary and the voting score. 5. The method of claim 1 , wherein the identifying the one or more object landmark candidates in the sequence of images comprises: identifying, by the computer, a first set of candidate samples included in the sequence of images; determining, by the computer, a first stage probability score for each candidate samples in the first set; identifying, by the computer, a second set of candidate samples from the first set based on the first stage probability scores; determining, by the computer, a second stage probability score for each of the candidate samples in the second set; and identifying, by the computer, the catheter object landmark candidates from the second set based on the second stage probability scores.

1. A method of font recognition comprising: calculating a plurality of feature values for a sample text, wherein the feature values correspond to a plurality of font features, and the plurality of font features includes curvature features for the sample text; determining a Euclidian distance between the plurality of the feature values for the sample text and respective model feature values for each of a plurality of predefined fonts, wherein the Euclidian distance for the i th predefined font is given by D i = ∑ j = 1 n ⁢ ⁢ ( T ij - V j ) 2 , where n is the number of feature values, T ij is an element corresponding to the i th row and j th column of a matrix of the model values, and V j is a j th element a vector containing the plurality of the feature values for the sample text; and signaling that the font of the sample text is the font from the plurality of predefined fonts corresponding to the smallest Euclidian distance.

1. A method of font recognition comprising: calculating a plurality of feature values for a sample text, wherein the feature values correspond to a plurality of font features, and the plurality of font features includes curvature features for the sample text; determining a Euclidian distance between the plurality of the feature values for the sample text and respective model feature values for each of a plurality of predefined fonts, wherein the Euclidian distance for the i th predefined font is given by D i = ∑ j = 1 n ⁢ ⁢ ( T ij - V j ) 2 , where n is the number of feature values, T ij is an element corresponding to the i th row and j th column of a matrix of the model values, and V j is a j th element a vector containing the plurality of the feature values for the sample text; and signaling that the font of the sample text is the font from the plurality of predefined fonts corresponding to the smallest Euclidian distance. 9. The method according to claim 1 , wherein the plurality of font features includes chain code direction features.

53. The RTVMM of claim 12 wherein the implementing step comprises the step: including an "activity pointer" field for each object in memory, the "activity pointer" identifying the real-time activity object that was responsible for allocation of the object, the "activity pointer" field containing a "null" value if the object was not allocated by a real-time activity; maintaining a finalizees list of objects waiting to be finalized for each real-time activity, the objects on the finalizees list being linked through the "activity pointer" field; maintaining a list of the headers of the finalizees lists, the pointer "finalizees" being a root pointer to the headers list.

53. The RTVMM of claim 12 wherein the implementing step comprises the step: including an "activity pointer" field for each object in memory, the "activity pointer" identifying the real-time activity object that was responsible for allocation of the object, the "activity pointer" field containing a "null" value if the object was not allocated by a real-time activity; maintaining a finalizees list of objects waiting to be finalized for each real-time activity, the objects on the finalizees list being linked through the "activity pointer" field; maintaining a list of the headers of the finalizees lists, the pointer "finalizees" being a root pointer to the headers list. 56. The RTVMM of claim 53 wherein the implementing step comprises the step: implementing a finalizer thread that is part of a real-time activity and is responsible for incrementally executing the finalizer methods associated with finalizee objects associated with the real-time activity and reachable from the "finalizees" pointer.

19. One or more computer-readable storage media comprising a plurality of instructions that in response to being executed cause a computing device to: update an index data structure based on an input data stream, wherein the index data structure includes index data associated with offsets in the input data stream; process a plurality of chunks of the input data stream in parallel to generate a plurality of token streams using the index data, wherein each chunk has a first length and each chunk overlaps a previous chunk by a second length, and wherein each token stream is generated from a corresponding disjoint subset of the plurality of chunks; and merge the plurality of token streams to generate an output token stream.

19. One or more computer-readable storage media comprising a plurality of instructions that in response to being executed cause a computing device to: update an index data structure based on an input data stream, wherein the index data structure includes index data associated with offsets in the input data stream; process a plurality of chunks of the input data stream in parallel to generate a plurality of token streams using the index data, wherein each chunk has a first length and each chunk overlaps a previous chunk by a second length, and wherein each token stream is generated from a corresponding disjoint subset of the plurality of chunks; and merge the plurality of token streams to generate an output token stream. 22. The one or more computer-readable storage media of claim 19 , wherein to merge the plurality of token streams to generate the output token stream comprises to: read a previous token and a next token from the plurality of token streams, wherein the previous token and the next token are consecutive with respect to the input data stream; determine whether the previous token and the next token originate from the same token stream; output the previous token to the output token stream in response to determining that the previous token and the next token originate from the same token stream; copy the next token to the previous token in response to outputting the previous token; read the next token from the plurality of token streams in response to copying the next token; and merge the previous token and the next token to generate one or more synchronized tokens in response to determining that the previous token and the next token do not originate from the same token stream.

7. A system comprising: at least one processor; and a memory storing instructions that when executed by the at least one processor cause the system to: configure a client device to: process one or more natural language inputs with respect to data sources stored on the client device to determine a first set of interpretation candidates for the one or more natural language inputs; and to communicate, to the system, results from processing the one or more natural language inputs with respect to the data sources stored on the client device; determine, based on the results from processing the one or more natural language inputs with respect to the data sources stored on the client device, a list of possible interpretation candidates for the one or more natural language inputs, the list comprising a second set of interpretation candidates for the one or more natural language inputs; rank the list of possible interpretation candidates; prune the list of possible interpretation candidates; constrain, based on pseudo data corresponding to the data sources located on the client device, the pruning to prevent at least one interpretation candidate of the second set of interpretation candidates from being pruned from the list of possible interpretation candidates; and communicate, to the client device, the second set of interpretation candidates for the one or more natural language inputs, for a final output interpretation of the one or more natural language inputs by the client device that comprises ranking a plurality of interpretation candidates comprising the first set of interpretation candidates and the second set of interpretation candidates.

7. A system comprising: at least one processor; and a memory storing instructions that when executed by the at least one processor cause the system to: configure a client device to: process one or more natural language inputs with respect to data sources stored on the client device to determine a first set of interpretation candidates for the one or more natural language inputs; and to communicate, to the system, results from processing the one or more natural language inputs with respect to the data sources stored on the client device; determine, based on the results from processing the one or more natural language inputs with respect to the data sources stored on the client device, a list of possible interpretation candidates for the one or more natural language inputs, the list comprising a second set of interpretation candidates for the one or more natural language inputs; rank the list of possible interpretation candidates; prune the list of possible interpretation candidates; constrain, based on pseudo data corresponding to the data sources located on the client device, the pruning to prevent at least one interpretation candidate of the second set of interpretation candidates from being pruned from the list of possible interpretation candidates; and communicate, to the client device, the second set of interpretation candidates for the one or more natural language inputs, for a final output interpretation of the one or more natural language inputs by the client device that comprises ranking a plurality of interpretation candidates comprising the first set of interpretation candidates and the second set of interpretation candidates. 9. The system of claim 7 , wherein the data sources comprise at least one of a music list stored on the client device, email content stored on the client device, meeting content stored on the client device, or a contact list stored on the client device.

10. A computer program product for capturing and managing knowledge from social networking interactions comprising: a non-transitory computer readable storage medium, said computer readable storage medium comprising computer readable program code embodied therewith, said computer readable program code comprising program instructions that, when executed, causes a processor to: present a marking element in a social networking interaction, wherein said marking element allows a user to specify whether a corresponding message corresponds to at least one member of the group consisting of a question and an answer; receive a first user selection indicating a portion of the social network interaction as a question; receive a second user selection indicating a portion of the social networking interaction as an answer; create a knowledge element in response to a user activating said marking element in said social networking interaction; store said knowledge element in a catalog of knowledge elements; present an evaluation element for evaluating said knowledge element in said social networking interaction; present an editing element for editing said knowledge element; present knowledge element indicators to accompany the corresponding messages, which knowledge element indicators indicate whether corresponding messages correspond to at least one of the group consisting of a question and an answer; alter said knowledge element in response to a user evaluating or editing said knowledge element; and recommend said knowledge element for use in response to a user composing a message relevant to said knowledge element in said social networking interaction before said user shares said message within said social networking interaction.

10. A computer program product for capturing and managing knowledge from social networking interactions comprising: a non-transitory computer readable storage medium, said computer readable storage medium comprising computer readable program code embodied therewith, said computer readable program code comprising program instructions that, when executed, causes a processor to: present a marking element in a social networking interaction, wherein said marking element allows a user to specify whether a corresponding message corresponds to at least one member of the group consisting of a question and an answer; receive a first user selection indicating a portion of the social network interaction as a question; receive a second user selection indicating a portion of the social networking interaction as an answer; create a knowledge element in response to a user activating said marking element in said social networking interaction; store said knowledge element in a catalog of knowledge elements; present an evaluation element for evaluating said knowledge element in said social networking interaction; present an editing element for editing said knowledge element; present knowledge element indicators to accompany the corresponding messages, which knowledge element indicators indicate whether corresponding messages correspond to at least one of the group consisting of a question and an answer; alter said knowledge element in response to a user evaluating or editing said knowledge element; and recommend said knowledge element for use in response to a user composing a message relevant to said knowledge element in said social networking interaction before said user shares said message within said social networking interaction. 14. The computer program product of claim 10 , further comprising program instructions that, when executed, cause said processor to rate a knowledge element based on whether the knowledge element is useful to a user.

1. A method performed by one or more processors of a computer system, the method comprising: determining, by the one or more processors, a plurality of titles embedded in or extracted from a corresponding plurality of documents; processing, by the one or more processors, a particular title, of the plurality of titles, by applying a first filter, of a plurality of filters, to generate a first score, the particular title corresponding to a particular document of the plurality of documents; processing, by the one or more processors, the particular title by applying a second filter, of the plurality of filters, to generate a second score, processing the particular title by applying the second filter, of the plurality of filters, including: determining a total count of script changes or font changes in the particular title, and generating the second score based on the total count of the script changes or the font changes in the particular title; assigning, by the one or more processors, a first weight to the first score to form a first weighted score; assigning, by the one or more processors, a second weight to the second score to form a second weighted score; calculating, by the one or more processors, a readability score based on the first weighted score and the second weighted score; determining, by the one or more processors, that the readability score satisfies a threshold score; generating or selecting a new title, for the particular document, based on determining that the readability score satisfies the threshold score; and assigning the new title to the particular document.

1. A method performed by one or more processors of a computer system, the method comprising: determining, by the one or more processors, a plurality of titles embedded in or extracted from a corresponding plurality of documents; processing, by the one or more processors, a particular title, of the plurality of titles, by applying a first filter, of a plurality of filters, to generate a first score, the particular title corresponding to a particular document of the plurality of documents; processing, by the one or more processors, the particular title by applying a second filter, of the plurality of filters, to generate a second score, processing the particular title by applying the second filter, of the plurality of filters, including: determining a total count of script changes or font changes in the particular title, and generating the second score based on the total count of the script changes or the font changes in the particular title; assigning, by the one or more processors, a first weight to the first score to form a first weighted score; assigning, by the one or more processors, a second weight to the second score to form a second weighted score; calculating, by the one or more processors, a readability score based on the first weighted score and the second weighted score; determining, by the one or more processors, that the readability score satisfies a threshold score; generating or selecting a new title, for the particular document, based on determining that the readability score satisfies the threshold score; and assigning the new title to the particular document. 5. The method of claim 1 , where processing the particular title by applying the first filter, of the plurality of filters, includes: detecting a measure of diversity of parts of speech included in the particular title, and generating the first score based on the measure of diversity of the parts of speech included in the particular title.

12. A computer program product comprising a computer usable medium having a computer readable program code embedded therein for controlling a data processing apparatus, the computer readable program code configured to cause the data processing apparatus to execute a process for generating a self-authenticating printed document, the process comprising: (a) receiving an original document image; (b) processing the original document image to generate processed data; (c) generating a barcode stamp element encoding a code calculated from the processed data generated in step (b); (d) generating a hierarchical barcode stamp by repeating the barcode stamp element in accordance with a pre-defined pattern; (e) controlling a printer to print the hierarchical barcode stamp and the original document image on a front side of a recording medium, wherein the hierarchical barcode stamp is printed with gray tone tiles and the original document image is printed in a black color, the hierarchical barcode stamp and the original document image overlapping each other such that at least some content of the original document image printed in the black color overlaps some gray tone tiles of the hierarchical barcode stamp.

12. A computer program product comprising a computer usable medium having a computer readable program code embedded therein for controlling a data processing apparatus, the computer readable program code configured to cause the data processing apparatus to execute a process for generating a self-authenticating printed document, the process comprising: (a) receiving an original document image; (b) processing the original document image to generate processed data; (c) generating a barcode stamp element encoding a code calculated from the processed data generated in step (b); (d) generating a hierarchical barcode stamp by repeating the barcode stamp element in accordance with a pre-defined pattern; (e) controlling a printer to print the hierarchical barcode stamp and the original document image on a front side of a recording medium, wherein the hierarchical barcode stamp is printed with gray tone tiles and the original document image is printed in a black color, the hierarchical barcode stamp and the original document image overlapping each other such that at least some content of the original document image printed in the black color overlaps some gray tone tiles of the hierarchical barcode stamp. 13. The computer program product of claim 12 , wherein step (c) comprises: generating a hash code from the processed data generated in step (b); and generating the barcode stamp element which encodes the hash code; and wherein step (d) comprises: generating the hierarchical barcode stamp from the barcode stamp element by repeating the barcode stamp element in accordance with the pre-defined pattern.

1. A method comprising: receiving, at an online system, a search phrase submitted in association with a user profile with the online system; identifying, by the online system, a first object with a first attribute that is connected to a second object in the online system with a second attribute from the search phrase; generating, by the online system, a structured query corresponding to the first object with the first attribute that is connected to the second object in the online system with the second attribute; performing, at a first time, a first search by the online system identifying a first set of objects with the first attribute that are each connected to another object in the online system having the second attribute; generating, for the structured query, a list of links to the first set of objects; performing, at a second time, a second search by the online system identifying a second set of objects with the first attribute that are each connected to another object having the second attribute; and updating the list of links for the structured query to include links to the second set of objects.

1. A method comprising: receiving, at an online system, a search phrase submitted in association with a user profile with the online system; identifying, by the online system, a first object with a first attribute that is connected to a second object in the online system with a second attribute from the search phrase; generating, by the online system, a structured query corresponding to the first object with the first attribute that is connected to the second object in the online system with the second attribute; performing, at a first time, a first search by the online system identifying a first set of objects with the first attribute that are each connected to another object in the online system having the second attribute; generating, for the structured query, a list of links to the first set of objects; performing, at a second time, a second search by the online system identifying a second set of objects with the first attribute that are each connected to another object having the second attribute; and updating the list of links for the structured query to include links to the second set of objects. 3. The method of claim 1 , further comprising: responsive to updating the list of links to include links to the second set of objects, incrementing a notification count for the search phrase.