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AIPD_nlp_sentence_dataset

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11. A method, comprising: A) storing, by one or more server computers communicatively coupled to a network, an electronic dictionary comprising one or more dictionary words; B) receiving, by said one or more server computers, a text string without spaces; C) determining, by said one or more server computers, whether a substring within said text string matches any of said one or more dictionary words; D) responsive to a determination that said substring matches any of said one or more dictionary words: i) identifying, by said one or more server computers, a substring position comprising a range of character positions of said substring within said text string; ii) appending, by said one or more server computers, said substring to a keyword array on said one or more sever computers; and iii) removing, by said one or more server computers, said substring from said text string; E) determining, by said one or more server computers, whether said text string comprises a remaining substring; F) responsive to a determination that said text string comprises said remaining substring, determining, by said one or more server computers, whether said remaining substring matches any of said one or more dictionary words; G) responsive to a determination that said remaining substring matches any of said one or more dictionary words, repeating, by said one or more server computers, steps i)-iv) for said remaining substring i) place the remaining substring in a temporary string; ii) identifying, by said one or more server computers, a remaining substring position comprising a range of character positions of said remaining substring within said text string; iii) appending, by said one or more server computers, said remaining substring to said keyword array on said one or more sever computers; and iv) removing, by said one or more server computers, said remaining substring from said temporary string; H) responsive to a determination that said remaining substring does not match any of said one or more dictionary words: i) identifying, by said one or more server computers, said remaining substring position for said remaining substring; and ii) appending, by said one or more server computers, said remaining substring to said keyword array; I) generating, by said one or more server computers, a keyword string comprising said keyword array ordered and parsed according to said substring position of said substring and said remaining substring position of said remaining substring; and J) transmitting, by said one or more server computers, said keyword string to a client computer communicatively coupled to said network.

11. A method, comprising: A) storing, by one or more server computers communicatively coupled to a network, an electronic dictionary comprising one or more dictionary words; B) receiving, by said one or more server computers, a text string without spaces; C) determining, by said one or more server computers, whether a substring within said text string matches any of said one or more dictionary words; D) responsive to a determination that said substring matches any of said one or more dictionary words: i) identifying, by said one or more server computers, a substring position comprising a range of character positions of said substring within said text string; ii) appending, by said one or more server computers, said substring to a keyword array on said one or more sever computers; and iii) removing, by said one or more server computers, said substring from said text string; E) determining, by said one or more server computers, whether said text string comprises a remaining substring; F) responsive to a determination that said text string comprises said remaining substring, determining, by said one or more server computers, whether said remaining substring matches any of said one or more dictionary words; G) responsive to a determination that said remaining substring matches any of said one or more dictionary words, repeating, by said one or more server computers, steps i)-iv) for said remaining substring i) place the remaining substring in a temporary string; ii) identifying, by said one or more server computers, a remaining substring position comprising a range of character positions of said remaining substring within said text string; iii) appending, by said one or more server computers, said remaining substring to said keyword array on said one or more sever computers; and iv) removing, by said one or more server computers, said remaining substring from said temporary string; H) responsive to a determination that said remaining substring does not match any of said one or more dictionary words: i) identifying, by said one or more server computers, said remaining substring position for said remaining substring; and ii) appending, by said one or more server computers, said remaining substring to said keyword array; I) generating, by said one or more server computers, a keyword string comprising said keyword array ordered and parsed according to said substring position of said substring and said remaining substring position of said remaining substring; and J) transmitting, by said one or more server computers, said keyword string to a client computer communicatively coupled to said network. 15. The method of claim 11 wherein said dictionary comprises a dictionary word database.

6. The method of claim 1 , wherein the imaging device comprises a mobile communication device.

6. The method of claim 1 , wherein the imaging device comprises a mobile communication device. 8. The method of claim 6 , wherein the computing device is remotely located with respect to the mobile communication device and the image of the at least one document containing tax data is transferred to the computing device over a network.

12. The method of claim 1 , further comprising: the learning personality component storing knowledge for subsequent response generation wherein the knowledge is generated from processing additional content attributable to the user, wherein the learning personality component stores knowledge for subsequent response generation by: processing additional content attributable to the user to generate usable knowledge; storing the usable knowledge in a knowledge base; and using the knowledge base to process content of the input message to determine one or more candidate responses.

12. The method of claim 1 , further comprising: the learning personality component storing knowledge for subsequent response generation wherein the knowledge is generated from processing additional content attributable to the user, wherein the learning personality component stores knowledge for subsequent response generation by: processing additional content attributable to the user to generate usable knowledge; storing the usable knowledge in a knowledge base; and using the knowledge base to process content of the input message to determine one or more candidate responses. 13. The method of claim 12 , wherein the processing further comprises: parsing the additional content to identify at least one response; extracting from the response terms and relationships between terms; and storing extracted term relationships in a term matrix within the knowledge base.

11. An apparatus for object detection and recognition comprising: memory and a processor; an input device coupled to the processor and configured to receive an image to undergo object detection and recognition; an input device coupled to the processor and configured to access a pre-specified classifier stored in the memory, the classifier configured to estimate a belief distribution over parts for each image element of the received image; a conditional random field model stored in the memory; and an inference mechanism coupled to the processor and configured to carry out an inference process on the conditional random field model to force a global part labeling which is substantially layout-consistent and thereby generate a part label map for the received image, the part label map comprising, for each image element of the received image, a label indicating which of a plurality of parts the image element is assigned to, each part being a densely represented image area; the processor being configured to: form the classifier during a training phase using a plurality of training images together with a mask for each training image indicating which pixels in the training image correspond to objects to be recognized and which correspond to background that is not required to be recognized; during the training phase, form an initial part label map for a training image by dividing the image into a plurality of parts having a consistent pair-wise ordering such that the parts contiguously cover the image; and ensure that the parts meet constraints related to image elements, the image elements being non-immediate neighbors.

11. An apparatus for object detection and recognition comprising: memory and a processor; an input device coupled to the processor and configured to receive an image to undergo object detection and recognition; an input device coupled to the processor and configured to access a pre-specified classifier stored in the memory, the classifier configured to estimate a belief distribution over parts for each image element of the received image; a conditional random field model stored in the memory; and an inference mechanism coupled to the processor and configured to carry out an inference process on the conditional random field model to force a global part labeling which is substantially layout-consistent and thereby generate a part label map for the received image, the part label map comprising, for each image element of the received image, a label indicating which of a plurality of parts the image element is assigned to, each part being a densely represented image area; the processor being configured to: form the classifier during a training phase using a plurality of training images together with a mask for each training image indicating which pixels in the training image correspond to objects to be recognized and which correspond to background that is not required to be recognized; during the training phase, form an initial part label map for a training image by dividing the image into a plurality of parts having a consistent pair-wise ordering such that the parts contiguously cover the image; and ensure that the parts meet constraints related to image elements, the image elements being non-immediate neighbors. 13. An apparatus as claimed in claim 11 wherein the conditional random field model comprises a hidden layer of part labels.

1. A system for scoring a comparison of ranked items associated with a topic selected by a first user, comprising: a processor; a topic module configured to cooperate with the processor to retrieve, over a communication network, one or more potential topics based on information stored in a social data repository of a first user, and receive a topic selection from the first user, the topic selection identifying a topic selected by the first user from the one or more potential topics; a GUI module configured to cooperate with the processor to present items associated with the topic to the first user to enable the first user to input a first ranking of a first set of the items according to a subjective preference of the first user and present the items to (i) a second user to enable the second user to input a second ranking of a second set of the items, and (ii) a third user to enable the third user to input a third ranking of the second set of the items; a ranking module configured to cooperate with the processor to receive (i) the first ranking of the first set of the items by the first user, (ii) the second ranking of the second set of the items by the second user, and (iii) the third ranking of the second set of the items by the third user; and a scoring module configured to cooperate with the processor to determine a first game score based on a comparison of the second ranking relative to the first ranking, and a second game score based on a comparison of the third ranking relative to the first ranking, the scoring module further configured to present an award based, at least in part, on a comparison of the first game score and the second game score.

1. A system for scoring a comparison of ranked items associated with a topic selected by a first user, comprising: a processor; a topic module configured to cooperate with the processor to retrieve, over a communication network, one or more potential topics based on information stored in a social data repository of a first user, and receive a topic selection from the first user, the topic selection identifying a topic selected by the first user from the one or more potential topics; a GUI module configured to cooperate with the processor to present items associated with the topic to the first user to enable the first user to input a first ranking of a first set of the items according to a subjective preference of the first user and present the items to (i) a second user to enable the second user to input a second ranking of a second set of the items, and (ii) a third user to enable the third user to input a third ranking of the second set of the items; a ranking module configured to cooperate with the processor to receive (i) the first ranking of the first set of the items by the first user, (ii) the second ranking of the second set of the items by the second user, and (iii) the third ranking of the second set of the items by the third user; and a scoring module configured to cooperate with the processor to determine a first game score based on a comparison of the second ranking relative to the first ranking, and a second game score based on a comparison of the third ranking relative to the first ranking, the scoring module further configured to present an award based, at least in part, on a comparison of the first game score and the second game score. 13. The system of claim 1 , wherein the scoring module is further configured to compare the first ranking and the second ranking to a predetermined ranking and score the first ranking and the second ranking based on the comparison.

76. The system of claim 75 wherein the flesh-out component is configured to insert function words in the ALR.

76. The system of claim 75 wherein the flesh-out component is configured to insert function words in the ALR. 83. The system of claim 76 wherein the flesh-out component inserts negators.

11. A non-transitory computer readable medium adapted to control an executable computer readable program code embodied therein, the executable computer readable program code for implementing a method of analyzing an electronic communication and generating a report of behavioral assessment data therefrom, the electronic communication being a telephonic communication and the method comprising: receiving a single telephonic communication between a first communicant to the telephonic communication and a second communicant to the telephonic communication; separating the telephonic communication into at least first constituent voice data and second constituent voice data, the first constituent voice data being generated by the first communicant and the second constituent voice data being generated by the second communicant; analyzing one of the separated first and second constituent voice data by mining the separated one of the first and second constituent voice data and applying a predetermined linguistic-based psychological behavioral model to the one of the separated first and second constituent voice data; and generating behavioral assessment data including personality type corresponding to the analyzed one of the separated first and second constituent voice data based on the step of analyzing one of the first and second constituent voice data.

11. A non-transitory computer readable medium adapted to control an executable computer readable program code embodied therein, the executable computer readable program code for implementing a method of analyzing an electronic communication and generating a report of behavioral assessment data therefrom, the electronic communication being a telephonic communication and the method comprising: receiving a single telephonic communication between a first communicant to the telephonic communication and a second communicant to the telephonic communication; separating the telephonic communication into at least first constituent voice data and second constituent voice data, the first constituent voice data being generated by the first communicant and the second constituent voice data being generated by the second communicant; analyzing one of the separated first and second constituent voice data by mining the separated one of the first and second constituent voice data and applying a predetermined linguistic-based psychological behavioral model to the one of the separated first and second constituent voice data; and generating behavioral assessment data including personality type corresponding to the analyzed one of the separated first and second constituent voice data based on the step of analyzing one of the first and second constituent voice data. 13. The non-transitory computer readable medium of claim 11 , further comprising an executable computer readable program code for implementing the step of storing the behavioral assessment data, the behavioral data corresponding to at least one identifying indicia and being made available for subsequent analysis.

7. The method of claim 1, wherein step (1) comprises the steps of: (a) writing one or more command files to a shared disk drive, said one or more command files each having a file name with a first file name extension; (b) renaming each of said one or more command files to a file name with a second file name extension after said one or more command files have been completely written to said shared disk drive; (c) retrieving from said shared disk drive any command files having file names with said second file name extension; and (d) executing said retrieved any command files, wherein said execution results in retrieving at least a portion of said source text document and at least a portion of said source image document.

7. The method of claim 1, wherein step (1) comprises the steps of: (a) writing one or more command files to a shared disk drive, said one or more command files each having a file name with a first file name extension; (b) renaming each of said one or more command files to a file name with a second file name extension after said one or more command files have been completely written to said shared disk drive; (c) retrieving from said shared disk drive any command files having file names with said second file name extension; and (d) executing said retrieved any command files, wherein said execution results in retrieving at least a portion of said source text document and at least a portion of said source image document. 8. The method of claim 7, wherein steps (a) and (b) are initiated by a first client computer and steps (c) and (d) are initiated by a second client computer.

1. A computerized method for evaluating a patent document, comprising: in a computer having a processor configured for: (a) introducing one or more patent indices, characterizing different aspects of the patent document, and a Patent Quality (PQ) index, depending on said one or more patent indices; a monetary value of the patent document being a function of said PQ index; (b) the Patent Quality index having a single numerical value, and being varied on a bounded interval for said PQ index having respective PQ min and PQ max values; each patent index having a single numerical value and being defined on a bounded interval for each said patent index having respective minimal and maximal values; and (c) transforming said one or more patent indices into said Patent Quality index according to a deterministic non-linear transformation; said non-linear transformation being continuous, monotonous with respect to each of said patent indices, said non-linear transformation being non-linear with respect to at least one of said patent indices; wherein said patent indices and said non-linear transformation are chosen so as to satisfy the following: tending of any one of said patent indices substantially to a respective minimal value, results in said Patent Quality index tending substantially to one of the following, independent of values of other patent indices: the PQ min ; the PQ max ; and wherein said non-linear transformation has a parameter of non-linearity expressed as a real number, and wherein said non-linear transformation is a single-valued transformation providing a single numerical value for said PQ index for any parameter of non-linearity.

1. A computerized method for evaluating a patent document, comprising: in a computer having a processor configured for: (a) introducing one or more patent indices, characterizing different aspects of the patent document, and a Patent Quality (PQ) index, depending on said one or more patent indices; a monetary value of the patent document being a function of said PQ index; (b) the Patent Quality index having a single numerical value, and being varied on a bounded interval for said PQ index having respective PQ min and PQ max values; each patent index having a single numerical value and being defined on a bounded interval for each said patent index having respective minimal and maximal values; and (c) transforming said one or more patent indices into said Patent Quality index according to a deterministic non-linear transformation; said non-linear transformation being continuous, monotonous with respect to each of said patent indices, said non-linear transformation being non-linear with respect to at least one of said patent indices; wherein said patent indices and said non-linear transformation are chosen so as to satisfy the following: tending of any one of said patent indices substantially to a respective minimal value, results in said Patent Quality index tending substantially to one of the following, independent of values of other patent indices: the PQ min ; the PQ max ; and wherein said non-linear transformation has a parameter of non-linearity expressed as a real number, and wherein said non-linear transformation is a single-valued transformation providing a single numerical value for said PQ index for any parameter of non-linearity. 12. The method as described in claim 1 , further comprising determining a monetary value of the patent document as a product of a normalized value of the PQ index equal to (PQ−PQ min )/(PQ max −PQ min ) and a monetary value of an Etalon Patent (EP) under current market conditions, the EP being devoted to the same problem as the patent document and characterized by the same set of patent indices, values of all patent indices of the EP being equal to respective maximal values.

23. The method of claim 15 wherein the processing step further comprises the processor computing an interestingness value based on the source data the derived features and at least one of the applicability conditions, and wherein the generating step comprises the processor generating the evaluation indicator based at least in part on the computed interestingness value.

23. The method of claim 15 wherein the processing step further comprises the processor computing an interestingness value based on the source data the derived features and at least one of the applicability conditions, and wherein the generating step comprises the processor generating the evaluation indicator based at least in part on the computed interestingness value. 25. The method of claim 23 wherein the evaluation indicator comprises a story generation request, the story generation request indicating that the narrative story is to be generated, the method further comprising the processor computing a priority value for the narrative story based at least in part on the computed interestingness value, and wherein the story generation request comprises the computed priority value.

1. A server computer, comprising: a processing unit; and memory coupled to the processing unit; the server computer configured to perform operations for updating language understanding classifier models, the operations comprising: receiving from at least one computing device of a plurality of computing devices communicatively coupled to the server computer, a first user selection of at least one of the following: at least one intent of a plurality of available intents and/or at least one slot for the at least one intent, wherein: the at least one intent is associated with at least one action used to perform at least one function of a category of functions for a domain; the at least one slot indicating a value used for performing the at least one action; and the first user selection associated with a digital voice input received at the at least one computing device; and upon receiving from at least another computing device of the plurality of computing devices, a plurality of subsequent user selections that are identical to the first user selection and a plurality of subsequent digital voice inputs corresponding to the plurality of subsequent user selections, wherein the plurality of subsequent digital voice inputs are substantially similar to the digital voice input: generating a labeled data set by pairing the digital voice input with the first user selection; selecting a language understanding classifier from a plurality of available language understanding classifiers associated with one or more agent definitions, the selecting based at least on the at least one intent; and updating the selected language understanding classifier based on the generated labeled data set.

1. A server computer, comprising: a processing unit; and memory coupled to the processing unit; the server computer configured to perform operations for updating language understanding classifier models, the operations comprising: receiving from at least one computing device of a plurality of computing devices communicatively coupled to the server computer, a first user selection of at least one of the following: at least one intent of a plurality of available intents and/or at least one slot for the at least one intent, wherein: the at least one intent is associated with at least one action used to perform at least one function of a category of functions for a domain; the at least one slot indicating a value used for performing the at least one action; and the first user selection associated with a digital voice input received at the at least one computing device; and upon receiving from at least another computing device of the plurality of computing devices, a plurality of subsequent user selections that are identical to the first user selection and a plurality of subsequent digital voice inputs corresponding to the plurality of subsequent user selections, wherein the plurality of subsequent digital voice inputs are substantially similar to the digital voice input: generating a labeled data set by pairing the digital voice input with the first user selection; selecting a language understanding classifier from a plurality of available language understanding classifiers associated with one or more agent definitions, the selecting based at least on the at least one intent; and updating the selected language understanding classifier based on the generated labeled data set. 2. The server computer according to claim 1 , the operations further comprising: determining a number of the plurality of subsequent user selections; and when the number of the plurality of subsequent user selections is higher than a first threshold, automatically updating the selected language understanding classifier based on the generated labeled data set.

1. A method of minimizing the risk of theft or disclosure of personally identifiable or sensitive information comprising the steps of: identifying data from a data source in a first stage of a search mechanism performed by a computer, said first stage identifying as potentially sensitive any data from said data source that exhibits any attributes relating to sensitive information; escalating said identified data to at least a second more sensitive stage of said search mechanism, said second stage including the step of scoring said identified data based on minimal information entropy; evaluating any identified data that includes a score beyond a predetermined threshold, said evaluating step utilizing a rule table to determine whether remediation is necessary; and remediating identified data in which remediation is determined to be necessary in said evaluating step; wherein said scoring step includes the step of using an information retrieval tool from the group consisting of Vector Space Models, Latent Semantic Analysis, Latent Dirichlet Allocation and Bayesian Networks to compare attributes of said data to attributes of similar concept data files; wherein said information retrieval tool is Vector Space Models and said step of using Vector Space Models comprises the step of voting by said similar concept data files to determine a classification for said data file; wherein said concept data files include clean data file and target data file classifications; and wherein said voting step further comprises the steps of: determining the N closest concept data files to said data; calculating a value representative of how close each of said N closest concept data files is relative to said data; summing separately values calculated for clean data files and for target data files; and classifying said data as clean or target based upon the relative values of clean data files and target data files obtained in said summing step.

1. A method of minimizing the risk of theft or disclosure of personally identifiable or sensitive information comprising the steps of: identifying data from a data source in a first stage of a search mechanism performed by a computer, said first stage identifying as potentially sensitive any data from said data source that exhibits any attributes relating to sensitive information; escalating said identified data to at least a second more sensitive stage of said search mechanism, said second stage including the step of scoring said identified data based on minimal information entropy; evaluating any identified data that includes a score beyond a predetermined threshold, said evaluating step utilizing a rule table to determine whether remediation is necessary; and remediating identified data in which remediation is determined to be necessary in said evaluating step; wherein said scoring step includes the step of using an information retrieval tool from the group consisting of Vector Space Models, Latent Semantic Analysis, Latent Dirichlet Allocation and Bayesian Networks to compare attributes of said data to attributes of similar concept data files; wherein said information retrieval tool is Vector Space Models and said step of using Vector Space Models comprises the step of voting by said similar concept data files to determine a classification for said data file; wherein said concept data files include clean data file and target data file classifications; and wherein said voting step further comprises the steps of: determining the N closest concept data files to said data; calculating a value representative of how close each of said N closest concept data files is relative to said data; summing separately values calculated for clean data files and for target data files; and classifying said data as clean or target based upon the relative values of clean data files and target data files obtained in said summing step. 15. The method as claimed in claim 1 said step of using Vector Space Models comprises the steps of: obtaining a corpus of concept data files that have been identified as possibly containing sensitive information; creating a matrix of attributes for clean concept data files within said corpus; and creating a matrix of attributes for target concept data files within said corpus.

3. The method of claim 1 , wherein resources in a session comprise video content, and wherein a watch time of a resource is a length of time that a user spent watching the video content.

3. The method of claim 1 , wherein resources in a session comprise video content, and wherein a watch time of a resource is a length of time that a user spent watching the video content. 4. The method of claim 3 , further comprising: computing a total of watch times for the session using pings received during the session, wherein each ping identifies a point in video content that a user has reached.

9. A model verification method by a computer system including a processor, a storage section, an input part, and an output part, the model verification method comprising: acquiring an original model figure data registered as a design pattern in association with a program to the storage section by the input part; converting the original model figure data by the processor into formal language expression data based upon an expression format of a predetermined formal language; adding formal language expression data of a derivative design by the processor, by adding a modification to a component of the formal language on the converted formal language expression data, and storing the formal language expression data of the derivative design to the storage section; and verifying the original model figure data with use of the generated information processor and outputting verifying result by the output part; the conversion to the formal language expression data includes: determining an expression format of a formal language into which the original model figure data are converted based upon an extension and/or type information defined at a header of a file, extracting a parameter that specifies the model figure among the original model figure data from characteristics of the determined formal language, generating the formal language expression data based upon the extracted parameter to verify the original model figure data with use of the generated information; acquiring a recommended Key performance indicator (KPI) range value, a KPI calculation function, and existing design log data based upon the matching result; calculating a KPI value of the matched information description expression data, to verify the model figure data with use of the calculated information; generating information for selecting a replacing candidate and a replaced candidate based upon the KPI value of the information description expression data, to verify the model figure data with use of the generated information, comparing KPI values that are equivalent and comparable in a plurality of pieces of the information description expression data quantified by the quantification part, setting information description expression data having a high KPI value as a replacing candidate, setting other information description expression data as replaced candidates, and store the relationship between the candidates, and calculating a ratio of KPI values of the replacing candidate and the replaced candidates and Return On Investment (ROI) value in a case of the replacement.

9. A model verification method by a computer system including a processor, a storage section, an input part, and an output part, the model verification method comprising: acquiring an original model figure data registered as a design pattern in association with a program to the storage section by the input part; converting the original model figure data by the processor into formal language expression data based upon an expression format of a predetermined formal language; adding formal language expression data of a derivative design by the processor, by adding a modification to a component of the formal language on the converted formal language expression data, and storing the formal language expression data of the derivative design to the storage section; and verifying the original model figure data with use of the generated information processor and outputting verifying result by the output part; the conversion to the formal language expression data includes: determining an expression format of a formal language into which the original model figure data are converted based upon an extension and/or type information defined at a header of a file, extracting a parameter that specifies the model figure among the original model figure data from characteristics of the determined formal language, generating the formal language expression data based upon the extracted parameter to verify the original model figure data with use of the generated information; acquiring a recommended Key performance indicator (KPI) range value, a KPI calculation function, and existing design log data based upon the matching result; calculating a KPI value of the matched information description expression data, to verify the model figure data with use of the calculated information; generating information for selecting a replacing candidate and a replaced candidate based upon the KPI value of the information description expression data, to verify the model figure data with use of the generated information, comparing KPI values that are equivalent and comparable in a plurality of pieces of the information description expression data quantified by the quantification part, setting information description expression data having a high KPI value as a replacing candidate, setting other information description expression data as replaced candidates, and store the relationship between the candidates, and calculating a ratio of KPI values of the replacing candidate and the replaced candidates and Return On Investment (ROI) value in a case of the replacement. 10. The model verification method as recited in claim 9 , wherein the generation of the formal language expression data of the derivative design includes: inserting any component to the formal language expression data to generate the formal language expression data of the derivative design, or replacing component of the formal language expression data with any component to generate the formal language expression data of the derivative design, or deleting any component and/or from the formal language expression data to generate the formal language expression data of the derivative design, or combining the above generated results to generate the formal language expression data of the derivative design.

5. The input and output device as claimed in claim 1 ; wherein the documents are received via e-mail or facsimile.

5. The input and output device as claimed in claim 1 ; wherein the documents are received via e-mail or facsimile. 7. The input and output device as claimed in claim 5 ; wherein, in the documents received via the facsimile, the documents stored in the storing part and printed are then deleted in a lump based on whether the stored documents have the printed mark.

18. Computer apparatus according to claim 12 , wherein the data formatting engine is arranged to form a data field association tree (D-fat) from the historical data.

18. Computer apparatus according to claim 12 , wherein the data formatting engine is arranged to form a data field association tree (D-fat) from the historical data. 19. Computer apparatus according to claim 18 , wherein the data formatting engine is arranged to construct and restructure the D-fat according to at least one user-defined criterion.

15. A apparatus comprising: one or more processors; a receiver configured to receive information about a proposed outgoing message stored in memory, the received information comprising text content of a body portion of the proposed outgoing message and metadata of the proposed outgoing message; a first classifier configured to classify the proposed outgoing message into one of a plurality of specified classes, to obtain an expected class of the proposed outgoing message based on the metadata of the proposed outgoing message and not the text content of the body portion of the proposed outgoing message, the first classifier being a multi-way classifier comprising N*(N-1)/2 sub-classifiers where N is the number of the plurality of specified classes and each sub-classifier provides a one-to-one classification determination between two specified classes, the metadata comprising one or more of: information on whether the proposed outgoing message is part of an email thread; information associated with a file attached to the proposed outgoing message; information in a subject line of the proposed outgoing message; information on voting buttons associated with the proposed outgoing message; and information on one or more required receipts associated with the proposed outgoing message; a second classifier different from the first classifier configured to obtain an actual class of the proposed outgoing message based on the text content of the body portion of the proposed outgoing message and not the metadata of the proposed outgoing message; a comparing unit configured to compare the expected class of the proposed outgoing message based on the metadata and the actual class of the proposed outgoing message based on the text content of the body portion; and a triggering unit configured to trigger an alert in response to the comparison failing to yield a match.

15. A apparatus comprising: one or more processors; a receiver configured to receive information about a proposed outgoing message stored in memory, the received information comprising text content of a body portion of the proposed outgoing message and metadata of the proposed outgoing message; a first classifier configured to classify the proposed outgoing message into one of a plurality of specified classes, to obtain an expected class of the proposed outgoing message based on the metadata of the proposed outgoing message and not the text content of the body portion of the proposed outgoing message, the first classifier being a multi-way classifier comprising N*(N-1)/2 sub-classifiers where N is the number of the plurality of specified classes and each sub-classifier provides a one-to-one classification determination between two specified classes, the metadata comprising one or more of: information on whether the proposed outgoing message is part of an email thread; information associated with a file attached to the proposed outgoing message; information in a subject line of the proposed outgoing message; information on voting buttons associated with the proposed outgoing message; and information on one or more required receipts associated with the proposed outgoing message; a second classifier different from the first classifier configured to obtain an actual class of the proposed outgoing message based on the text content of the body portion of the proposed outgoing message and not the metadata of the proposed outgoing message; a comparing unit configured to compare the expected class of the proposed outgoing message based on the metadata and the actual class of the proposed outgoing message based on the text content of the body portion; and a triggering unit configured to trigger an alert in response to the comparison failing to yield a match. 17. The apparatus as claimed in claim 15 further comprising a feedback receiver configured to receive feedback about the proposed outgoing message and an updater configured to use the feedback to update at least one of the first or second classifiers.

4. The method according to claim 1 wherein: the computer system is a vehicle navigation system; the database of names is a navigation database of street addresses, cities, and states; and the name spelled by the user is a destination address containing a street number, a street name, a city name, and a state name.

4. The method according to claim 1 wherein: the computer system is a vehicle navigation system; the database of names is a navigation database of street addresses, cities, and states; and the name spelled by the user is a destination address containing a street number, a street name, a city name, and a state name. 5. The method according to claim 4 wherein the destination address is resolved by: comparing the state name as spelled by the user against a list of states in the United States to determine a matched state name; using the matched state name to create a subset of possible city names from the navigation database, where the subset of possible city names includes all of the cities in the state designated by the matched state name; comparing the city name as spelled by the user against the subset of possible city names to determine a matched city name; using the matched city name to create a subset of possible street names from the navigation database, where the subset of possible street names includes all of the streets in the city designated by the matched city name; comparing the street name as spelled by the user against the subset of possible street names to determine a matched street name; reconstructing the destination address by appending the matched street name to the street number as spoken by the user, adding the matched city name, and adding the matched state name; and presenting the destination address as reconstructed to the user for confirmation.

3. The computer program product of claim 2 , further comprising code for identifying an identifier associated with the at least one of the content portions of the electronic document responsively to the correspondent request for or serving of the electronic document.

3. The computer program product of claim 2 , further comprising code for identifying an identifier associated with the at least one of the content portions of the electronic document responsively to the correspondent request for or serving of the electronic document. 4. The computer program product of claim 3 , further comprising code for serving the identified identifier contemporaneously with the electronic document, wherein the served identifier forms at least a part of the at least one link.

4. A method of selecting a page to be associated with content, comprising: determining a plurality of pages each relating to specified content corresponding to a search request by a user for a particular type of item; verifying an identity of the user; analyzing information associated with each of the plurality of pages using a plurality of scalable algorithms, each algorithm generating a ranking score for each of the pages each algorithm in the plurality of scalable algorithms being different from the other algorithms; receiving at least one goal selected by a category manager to be used in determining one of the selected pages to be associated with the specified content, wherein the at least one goal is one of revenue optimization, profitability optimization, and visibility optimization: selecting, based on at least a plurality of the ranking scores generated for each of the plurality of pages an optimal page from the selected pages with respect to the at least one goal; and associating the content with the optimal page, wherein the user accessing the content is provided with a navigational element for navigating to the optimal page and receiving additional information to the particular type of item.

4. A method of selecting a page to be associated with content, comprising: determining a plurality of pages each relating to specified content corresponding to a search request by a user for a particular type of item; verifying an identity of the user; analyzing information associated with each of the plurality of pages using a plurality of scalable algorithms, each algorithm generating a ranking score for each of the pages each algorithm in the plurality of scalable algorithms being different from the other algorithms; receiving at least one goal selected by a category manager to be used in determining one of the selected pages to be associated with the specified content, wherein the at least one goal is one of revenue optimization, profitability optimization, and visibility optimization: selecting, based on at least a plurality of the ranking scores generated for each of the plurality of pages an optimal page from the selected pages with respect to the at least one goal; and associating the content with the optimal page, wherein the user accessing the content is provided with a navigational element for navigating to the optimal page and receiving additional information to the particular type of item. 11. A method according to claim 4 , further comprising: weighting each goal when more than one goal is received for the specified content.

1. A computer-implemented method for aurally scrolling an information source, comprising: analyzing an information source; wherein the information source comprises a plurality of markup tags; wherein analyzing the information source comprises using the plurality of markup tags to identify a plurality of segments of the information source from which to derive corresponding marker texts; generating and storing, separate from the information source, a set of a plurality of marker texts based at least on the analyzing of the information source including generating each marker text in the set of marker texts based at least on an analysis of a corresponding segment, of the plurality of identified segments, of the information source; wherein the analysis of a particular segment, of the plurality of identified segments, corresponding to a particular marker text of the set of marker texts comprises applying a summarization technique to the particular segment to derive the particular marker text; wherein the analysis of the particular segment comprises determining a significance of the particular segment based at least in part on a relative amount of text content of the particular segment; generating and storing data that comprises, for each marker text in the set of marker texts, an association between the marker text and a location within the information source, the location corresponding to the segment of the information source that corresponds to the marker text; arranging the plurality of marker texts in a sequence, the particular marker text having an order in the sequence; wherein the order of the particular marker text in the sequence is dependent on the determined significance of the particular segment that was determined based at least in part on the relative amount of text content of the particular segment; initiating an aural presentation of the sequence, the aural presentation comprising computerized text-to-speech synthesis of at least a portion of the sequence; during the aural presentation of the sequence, receiving input while the particular marker text of the set of marker texts is being aurally presented; and in response to the input: ceasing the aural presentation of the particular marker text; inspecting the data to identify the location associated with the particular marker text; and initiating an aural presentation of the information source at the location associated with the particular marker text, the aural presentation comprising computerized text-to-speech synthesis of at least a portion of the information source; wherein the method is performed by one or more computing devices.

1. A computer-implemented method for aurally scrolling an information source, comprising: analyzing an information source; wherein the information source comprises a plurality of markup tags; wherein analyzing the information source comprises using the plurality of markup tags to identify a plurality of segments of the information source from which to derive corresponding marker texts; generating and storing, separate from the information source, a set of a plurality of marker texts based at least on the analyzing of the information source including generating each marker text in the set of marker texts based at least on an analysis of a corresponding segment, of the plurality of identified segments, of the information source; wherein the analysis of a particular segment, of the plurality of identified segments, corresponding to a particular marker text of the set of marker texts comprises applying a summarization technique to the particular segment to derive the particular marker text; wherein the analysis of the particular segment comprises determining a significance of the particular segment based at least in part on a relative amount of text content of the particular segment; generating and storing data that comprises, for each marker text in the set of marker texts, an association between the marker text and a location within the information source, the location corresponding to the segment of the information source that corresponds to the marker text; arranging the plurality of marker texts in a sequence, the particular marker text having an order in the sequence; wherein the order of the particular marker text in the sequence is dependent on the determined significance of the particular segment that was determined based at least in part on the relative amount of text content of the particular segment; initiating an aural presentation of the sequence, the aural presentation comprising computerized text-to-speech synthesis of at least a portion of the sequence; during the aural presentation of the sequence, receiving input while the particular marker text of the set of marker texts is being aurally presented; and in response to the input: ceasing the aural presentation of the particular marker text; inspecting the data to identify the location associated with the particular marker text; and initiating an aural presentation of the information source at the location associated with the particular marker text, the aural presentation comprising computerized text-to-speech synthesis of at least a portion of the information source; wherein the method is performed by one or more computing devices. 9. The computer-implemented method as recited in claim 1 , wherein the input comprises at least one of an aural input and a text based input.

1. A system for data binding, comprising: a microprocessor; a schema object model, wherein the schema object model is an object oriented programming language object model that directly models a schema that includes one or more schema definition language types based on a schema definition language, and wherein the schema object model allows manipulation of schema documents in the schema definition language, wherein the schema object model programmatically creates at least one new schema definition language type in the schema based on the one or more schema definition language types pre-defined in the schema definition language, wherein the at least one new schema definition language type is not pre-defined in the schema definition language, and wherein the schema object model supports a pool of empty object trees that are ready to be filled with one or more object oriented programming language objects based on the schema; a schema compiler adapted to accept the schema associated with an Extensible Markup Language (XML) document and generate a set of interfaces that map the one or more schema definition language types of the schema into object-oriented programming language classes using the schema object model with the pool of empty object trees; and an Application Programming Interface (API) to map between the XML document and the object-oriented programming language classes based on at least one empty object tree from the pool of empty object trees.

1. A system for data binding, comprising: a microprocessor; a schema object model, wherein the schema object model is an object oriented programming language object model that directly models a schema that includes one or more schema definition language types based on a schema definition language, and wherein the schema object model allows manipulation of schema documents in the schema definition language, wherein the schema object model programmatically creates at least one new schema definition language type in the schema based on the one or more schema definition language types pre-defined in the schema definition language, wherein the at least one new schema definition language type is not pre-defined in the schema definition language, and wherein the schema object model supports a pool of empty object trees that are ready to be filled with one or more object oriented programming language objects based on the schema; a schema compiler adapted to accept the schema associated with an Extensible Markup Language (XML) document and generate a set of interfaces that map the one or more schema definition language types of the schema into object-oriented programming language classes using the schema object model with the pool of empty object trees; and an Application Programming Interface (API) to map between the XML document and the object-oriented programming language classes based on at least one empty object tree from the pool of empty object trees. 10. A system according to claim 1 , further comprising a schema parser for parsing the schema and generating the schema object model.

8. A system comprising: A. a database communicatively coupled to a network and comprising: i. a plurality of potential search terms; ii. a plurality of attributes for each of the plurality of potential search terms; and iii. one or more conceptual classes comprising the plurality of potential search terms, wherein the plurality of potential search terms within a conceptual class share a plurality of common attributes; B. a client computer communicatively coupled to the network and comprising a client software configured to receive a plurality of search terms; and C. a server computer communicatively coupled to the network and comprising a server software configured to: i. establish an instance of an individual concept for each of the plurality of search terms, wherein the instance of the individual concept comprises a meaning for each of the plurality of search terms and wherein the meaning is derived from a context of the plurality of search terms; ii. identify one or more conceptual classes which share one or more of the plurality of common attributes with the individual concept for each of the plurality of search terms; iii. organize the one or more conceptual classes identified into a hierarchy structure comprising one or more subsuming classes and one or more subsumed classes, wherein the one or more subsumed classes inherit the plurality of attributes from the one or more subsuming classes and wherein the one or more subsumed classes comprise one or more localized polymorphic changes to the plurality of attributes; iv. identify a hierarchy level at which all of the plurality of attributes of the individual concept are not common to all of the plurality of attributes of the plurality of potential search terms in the one or more subsumed classes; v. return a plurality of search results derived by searching all of the plurality of potential search terms on the hierarchy level; and vi send the search results to the client software for display.

8. A system comprising: A. a database communicatively coupled to a network and comprising: i. a plurality of potential search terms; ii. a plurality of attributes for each of the plurality of potential search terms; and iii. one or more conceptual classes comprising the plurality of potential search terms, wherein the plurality of potential search terms within a conceptual class share a plurality of common attributes; B. a client computer communicatively coupled to the network and comprising a client software configured to receive a plurality of search terms; and C. a server computer communicatively coupled to the network and comprising a server software configured to: i. establish an instance of an individual concept for each of the plurality of search terms, wherein the instance of the individual concept comprises a meaning for each of the plurality of search terms and wherein the meaning is derived from a context of the plurality of search terms; ii. identify one or more conceptual classes which share one or more of the plurality of common attributes with the individual concept for each of the plurality of search terms; iii. organize the one or more conceptual classes identified into a hierarchy structure comprising one or more subsuming classes and one or more subsumed classes, wherein the one or more subsumed classes inherit the plurality of attributes from the one or more subsuming classes and wherein the one or more subsumed classes comprise one or more localized polymorphic changes to the plurality of attributes; iv. identify a hierarchy level at which all of the plurality of attributes of the individual concept are not common to all of the plurality of attributes of the plurality of potential search terms in the one or more subsumed classes; v. return a plurality of search results derived by searching all of the plurality of potential search terms on the hierarchy level; and vi send the search results to the client software for display. 15. The system of claim 8 further comprising an input knowledge agent, a results engine, an ontology language, a clustering algorithm, an output knowledge agent or any combination thereof.

1. A method for identifying a primary document version of a document from a plurality of document versions of the document, the method, executed by a computer system and comprising: storing, by the computer system, a source-priority list comprising a plurality of document sources, each source having an associated priority of authority, where the priorities include higher priorities and lower priorities; accessing, by the computer system, at least one data repository to obtain metadata for each document version of the document versions; determining, by the computer system and based on the metadata, a source of each document version; determining, by the computer system, from the source-priority list, a priority of authority for the source of each document version; selecting, by the computer system, a document version, of the document versions, having the source with a highest priority of authority and a qualified citation count based on a citation count measure; providing, by the computer system, the document version having the source with a highest priority of authority for presentation.

1. A method for identifying a primary document version of a document from a plurality of document versions of the document, the method, executed by a computer system and comprising: storing, by the computer system, a source-priority list comprising a plurality of document sources, each source having an associated priority of authority, where the priorities include higher priorities and lower priorities; accessing, by the computer system, at least one data repository to obtain metadata for each document version of the document versions; determining, by the computer system and based on the metadata, a source of each document version; determining, by the computer system, from the source-priority list, a priority of authority for the source of each document version; selecting, by the computer system, a document version, of the document versions, having the source with a highest priority of authority and a qualified citation count based on a citation count measure; providing, by the computer system, the document version having the source with a highest priority of authority for presentation. 5. The method of claim 1 , where selecting the document version having the source with a highest priority of authority further comprises: selecting a document version having both a highest priority of authority and a qualified reference count based on a reference count measure.

13. A device, comprising: a processor; and a memory having stored thereon instructions that, when executed by the processor, cause the processor to perform operations comprising: identifying a message profile associated with a voice message; selecting an abbreviation library associated with the message profile; producing a text representation of the voice message; and compacting the text representation using the abbreviation library selected to produce a compact text representation, wherein the compact text representation includes an abbreviation from the abbreviation library selected, and an extent to which the voice message is compacted is based on network capacity and a subscriber profile.

13. A device, comprising: a processor; and a memory having stored thereon instructions that, when executed by the processor, cause the processor to perform operations comprising: identifying a message profile associated with a voice message; selecting an abbreviation library associated with the message profile; producing a text representation of the voice message; and compacting the text representation using the abbreviation library selected to produce a compact text representation, wherein the compact text representation includes an abbreviation from the abbreviation library selected, and an extent to which the voice message is compacted is based on network capacity and a subscriber profile. 17. The device of claim 13 , wherein the instructions further cause the processor to provide an expanded text associated with the compact text representation.

6. The character recognition system according to claim 5 wherein the identification of the at least one first display font and the at least one first character size of the at least one first character is carried out by searching at least one second recognition dictionary that is a subset of the at least one first recognition dictionary and that has information pertaining to less characters than the at least one first recognition dictionary.

6. The character recognition system according to claim 5 wherein the identification of the at least one first display font and the at least one first character size of the at least one first character is carried out by searching at least one second recognition dictionary that is a subset of the at least one first recognition dictionary and that has information pertaining to less characters than the at least one first recognition dictionary. 8. The character recognition system according to claim 6 wherein the at least one second recognition dictionary contains records for not more than five check characters.

9. A handheld electronic device comprising: an input apparatus; an output apparatus; and a processor apparatus comprising a processor and a memory, wherein the processor apparatus is structured to: detect an ambiguous character-string input that comprises a current character input and a previous character input; generate a plurality of character permutations of the ambiguous character-string input, at least one of the character permutations being a potential artificial variant that is neither a prefix of a word object nor is identical to a word object; output at least one of the character permutations other than the potential artificial variant; determine that the potential artificial variant has been displayed during a current session; based on the determination that the potential artificial variant has been displayed during a current session, output a displayed artificial variant as a representation of the potential artificial variant, wherein the displayed artificial variant is outputted at a position of relatively lower priority than at least one of the outputted character permutations; determine that the displayed artificial variant is not selected; and based on the determination that the displayed artificial variant is not selected, suppress from being output an offspring artificial variant of the unselected artificial variant when a next character input associated with the ambiguous character-string is detected.

9. A handheld electronic device comprising: an input apparatus; an output apparatus; and a processor apparatus comprising a processor and a memory, wherein the processor apparatus is structured to: detect an ambiguous character-string input that comprises a current character input and a previous character input; generate a plurality of character permutations of the ambiguous character-string input, at least one of the character permutations being a potential artificial variant that is neither a prefix of a word object nor is identical to a word object; output at least one of the character permutations other than the potential artificial variant; determine that the potential artificial variant has been displayed during a current session; based on the determination that the potential artificial variant has been displayed during a current session, output a displayed artificial variant as a representation of the potential artificial variant, wherein the displayed artificial variant is outputted at a position of relatively lower priority than at least one of the outputted character permutations; determine that the displayed artificial variant is not selected; and based on the determination that the displayed artificial variant is not selected, suppress from being output an offspring artificial variant of the unselected artificial variant when a next character input associated with the ambiguous character-string is detected. 12. The device of claim 9 , wherein the processor is further structured to make a determination that a final set of characters of the potential artificial variant corresponds with an N-gram object associated with a frequency object having a frequency value above a predetermined threshold.

28. An apparatus for index extraction, comprising: a database stored in a memory, the database including a plurality of ground truth documents, the documents being organized according to a plurality of classifications, each classification having a group of predefined indices; a document to be indexed stored in the memory, the document being associated with one of the classifications; means for attempting to extract from the document at least a subset of the group of predefined indices associated with the one of the classifications; and means for attempting to find and correct at least one text recognition error in the document based upon a salient dictionary associated with the one of the classifications upon a failure to extract the subset of the group of predefined indices, wherein anticipated misspellings associated with each of the classifications are stored in the salient dictionary and the document is searched for anticipated misspellings of predefined indices that have not been extracted from the document.

28. An apparatus for index extraction, comprising: a database stored in a memory, the database including a plurality of ground truth documents, the documents being organized according to a plurality of classifications, each classification having a group of predefined indices; a document to be indexed stored in the memory, the document being associated with one of the classifications; means for attempting to extract from the document at least a subset of the group of predefined indices associated with the one of the classifications; and means for attempting to find and correct at least one text recognition error in the document based upon a salient dictionary associated with the one of the classifications upon a failure to extract the subset of the group of predefined indices, wherein anticipated misspellings associated with each of the classifications are stored in the salient dictionary and the document is searched for anticipated misspellings of predefined indices that have not been extracted from the document. 30. The apparatus of claim 28 , further comprising means for reclassifying the document upon a failure to extract at least the subset of the group of predefined indices from the document.

9. A computer program product comprising a computer readable storage medium storing a computer readable program, wherein the computer readable program when executed by a processor on a computer causes the computer to: while building the search index and using the search index to respond to one or more search requests and performing synchronous anchor text processing, maintain an anchor information store, wherein each entry of the anchor information store identifies a referring document, a target document, and anchor text associated with a link from the referring document to the target document; maintain a rebuild agenda, wherein each entry of the rebuild agenda identifies a target document that has an entry in the search index and whose anchor text is to be updated in the search index with asynchronous processing because there is at least one new or updated link pointing to the target document; receive a document for processing; for each outgoing link in the document that points to a target document, add an entry to the anchor information store that identifies the received document, the target document, and anchor text; and add an entry to the rebuild agenda for the target document; and for each link pointing from a referring document to the document, locate one or more entries in the anchor information store for which the received document to be processed is identified as the target document; retrieve anchor text from each of the identified entries; and store the retrieved anchor text in an entry of the search index for the received document; and perform asynchronous anchor text processing to incrementally update current entries in the search index for each document identified in each entry in the rebuild agenda in parallel with the building of the search index and in parallel with using the search index to respond to one or more search requests by: selecting a first target document in the rebuild agenda; using the anchor information store to find anchor text for the first target document by identifying one or more entries in the anchor information store for which the first target document is identified as the target document in the anchor information store; retrieving anchor text from each of the identified entries; and updating the anchor text in the entry of the search index for the first target document.

9. A computer program product comprising a computer readable storage medium storing a computer readable program, wherein the computer readable program when executed by a processor on a computer causes the computer to: while building the search index and using the search index to respond to one or more search requests and performing synchronous anchor text processing, maintain an anchor information store, wherein each entry of the anchor information store identifies a referring document, a target document, and anchor text associated with a link from the referring document to the target document; maintain a rebuild agenda, wherein each entry of the rebuild agenda identifies a target document that has an entry in the search index and whose anchor text is to be updated in the search index with asynchronous processing because there is at least one new or updated link pointing to the target document; receive a document for processing; for each outgoing link in the document that points to a target document, add an entry to the anchor information store that identifies the received document, the target document, and anchor text; and add an entry to the rebuild agenda for the target document; and for each link pointing from a referring document to the document, locate one or more entries in the anchor information store for which the received document to be processed is identified as the target document; retrieve anchor text from each of the identified entries; and store the retrieved anchor text in an entry of the search index for the received document; and perform asynchronous anchor text processing to incrementally update current entries in the search index for each document identified in each entry in the rebuild agenda in parallel with the building of the search index and in parallel with using the search index to respond to one or more search requests by: selecting a first target document in the rebuild agenda; using the anchor information store to find anchor text for the first target document by identifying one or more entries in the anchor information store for which the first target document is identified as the target document in the anchor information store; retrieving anchor text from each of the identified entries; and updating the anchor text in the entry of the search index for the first target document. 13. The computer program product of claim 9 , wherein the computer readable program when executed on a computer causes the computer to: generate a graph that shows connections of nodes using the anchor information store to enable studying site connectedness.

1. A method comprising: identifying a plurality of web pages resulting from a search based on a search query; employing at least one processor to automatically extract one or more item lists from one or more web pages of the plurality of web pages based at least on one or more list pattern identification techniques, the one or more list pattern identification techniques including identifying terms that are in parallel positions in a sentence or continuous lines according to a text pattern in a respective web page of the one or more web pages as a respective item list of the one or more item lists, the respective item list of the one or more item lists including two or more related words or phrases included in the respective web page of the one or more web pages, wherein at least one of the two or more related words or phrases is related to the search query and does not include a keyword for the search query; weighting the one or more item lists; clustering the one or more weighted item lists based on a determination that at least some of the item lists include one or more similar items; and generating, based at least in part on the clustering, one or more query dimensions that summarize an aspect of the search query, at least one of the one or more query dimensions comprising a plurality of items from a cluster of weighted item lists.

1. A method comprising: identifying a plurality of web pages resulting from a search based on a search query; employing at least one processor to automatically extract one or more item lists from one or more web pages of the plurality of web pages based at least on one or more list pattern identification techniques, the one or more list pattern identification techniques including identifying terms that are in parallel positions in a sentence or continuous lines according to a text pattern in a respective web page of the one or more web pages as a respective item list of the one or more item lists, the respective item list of the one or more item lists including two or more related words or phrases included in the respective web page of the one or more web pages, wherein at least one of the two or more related words or phrases is related to the search query and does not include a keyword for the search query; weighting the one or more item lists; clustering the one or more weighted item lists based on a determination that at least some of the item lists include one or more similar items; and generating, based at least in part on the clustering, one or more query dimensions that summarize an aspect of the search query, at least one of the one or more query dimensions comprising a plurality of items from a cluster of weighted item lists. 4. The method of claim 1 , wherein the one or more list pattern identification techniques further comprise identifying one or more metadata tag patterns within the one or more web pages.

21. A work station comprising: a memory; a user mapping input interface, for receiving a mapping from a user; an export command input interface for triggering an export of a design; a system font selection interface, for receiving a system font selection from said user; and a design display interface, for displaying a graphical representation of said design; wherein said design display interface and said system font selection interface allow said user to apply a system font to said design; said mapping maps said system font to an alternative font; said system font is linked to said design: (i) continuously before and after said mapping is received from said user, and (ii) using an encoding prior to said export of said design; and said alternative font is linked to said design using a different encoding after said export of said design; said design is not displayed using the alternative font while the design is displayed in the design display interface; and said design is displayed using said alternative font while said design is rendered in an external player or a virtual external player.

21. A work station comprising: a memory; a user mapping input interface, for receiving a mapping from a user; an export command input interface for triggering an export of a design; a system font selection interface, for receiving a system font selection from said user; and a design display interface, for displaying a graphical representation of said design; wherein said design display interface and said system font selection interface allow said user to apply a system font to said design; said mapping maps said system font to an alternative font; said system font is linked to said design: (i) continuously before and after said mapping is received from said user, and (ii) using an encoding prior to said export of said design; and said alternative font is linked to said design using a different encoding after said export of said design; said design is not displayed using the alternative font while the design is displayed in the design display interface; and said design is displayed using said alternative font while said design is rendered in an external player or a virtual external player. 23. The work station of claim 21 , wherein: said user mapping input interface allows said user to select between a set of two options for defining said alternative font; and said set of two options consists of: (i) manually inputting a set of style sheet language properties and (ii) using a web font pointer.

2. A method for adaptive query execution plan enhancement by a computer system comprising a processor, comprising: selecting a sample of literal sets from an execution history of a query statement; determining a plurality of access paths by applying each literal set in the sample to the query statement, comprising, for each given literal set in the sample: generating a new query statement by substituting variables in the query statement with the given literal set; explaining the new query statement to obtain a given critical access path; associating the given critical access path with the given literal set; incrementing a hit count associated with the given critical access path; and incrementing a total sampling count associated with the query statement; for each given access path of the plurality of access paths, determining a total execution cost by applying each literal set in the sample to the given access path; and selecting a preferred access path from the plurality of access paths based on the total execution costs for each given access path.

2. A method for adaptive query execution plan enhancement by a computer system comprising a processor, comprising: selecting a sample of literal sets from an execution history of a query statement; determining a plurality of access paths by applying each literal set in the sample to the query statement, comprising, for each given literal set in the sample: generating a new query statement by substituting variables in the query statement with the given literal set; explaining the new query statement to obtain a given critical access path; associating the given critical access path with the given literal set; incrementing a hit count associated with the given critical access path; and incrementing a total sampling count associated with the query statement; for each given access path of the plurality of access paths, determining a total execution cost by applying each literal set in the sample to the given access path; and selecting a preferred access path from the plurality of access paths based on the total execution costs for each given access path. 6. The method of claim 2 , further comprising: collecting a plurality of preferred access paths for a plurality of query statements in an application workload; and presenting the plurality of preferred access paths as a query execution plan enhancement recommendation.

15. The computer implemented method of claim 14 wherein the method is a method for automatically generating one or more aspects of the architectural contract document for the building based on one or more values for dimensions for architectural features from the architect entered into the form specific to the architectural work category, wherein the one or more values are received after the owner of the building has provided the architect with the proposed use and approximate size for the building, wherein the architect is responsible for producing a design for the building based on the proposed use and approximate size provided by the owner, and wherein the architectural contract document comprises plans for the building.

15. The computer implemented method of claim 14 wherein the method is a method for automatically generating one or more aspects of the architectural contract document for the building based on one or more values for dimensions for architectural features from the architect entered into the form specific to the architectural work category, wherein the one or more values are received after the owner of the building has provided the architect with the proposed use and approximate size for the building, wherein the architect is responsible for producing a design for the building based on the proposed use and approximate size provided by the owner, and wherein the architectural contract document comprises plans for the building. 16. The computer implemented method of claim 15 wherein the architect is responsible for creating the plans for the building.

3. A method of processing natural language text on a computer for connected to a computer network and having computer-executable instructions for performing a method to identify text having specific semantic meaning, comprising the steps of: (a) loading onto a non-transitory computer readable medium an input list of words in order of decreasing frequency of use; (b) determining which words with which to associate rules based on the frequency of use of words loaded in step (a); (c) associating rules with the least frequently used word(s) that the rules require to be satisfied; (d) converting the input words to hashed tokens; (e) converting the tokens created in step (d) to numbers indicating the particular word each token represents; (f) collecting the rules associated with each word; (g) queuing the rules gathered in step (f) into appropriate priority-ordered queues of rules to perform; (h) evaluating the first rule on the highest priority queue; (i) repeating step (h) until all rules have been evaluated; (j) returning the results step (i) as output; and (k) passing the output of step (j) onto a next stage of processing.

3. A method of processing natural language text on a computer for connected to a computer network and having computer-executable instructions for performing a method to identify text having specific semantic meaning, comprising the steps of: (a) loading onto a non-transitory computer readable medium an input list of words in order of decreasing frequency of use; (b) determining which words with which to associate rules based on the frequency of use of words loaded in step (a); (c) associating rules with the least frequently used word(s) that the rules require to be satisfied; (d) converting the input words to hashed tokens; (e) converting the tokens created in step (d) to numbers indicating the particular word each token represents; (f) collecting the rules associated with each word; (g) queuing the rules gathered in step (f) into appropriate priority-ordered queues of rules to perform; (h) evaluating the first rule on the highest priority queue; (i) repeating step (h) until all rules have been evaluated; (j) returning the results step (i) as output; and (k) passing the output of step (j) onto a next stage of processing. 6. The method of claim 3 , wherein the words in step (c) are represented by numbers that reflect their relative usage, wherein with the most commonly used word “the” is represented by the number 1.

1. A method for trait prediction comprising: receiving a text sequence comprising words, each of the words including at least one character, some of the words comprising more than one character; generating a character embeddings for each of the characters of the text sequence, each character embedding being a multidimensional representation of a respective one of the characters; generating word representations for words of the text sequence with a trained character sequence model, based on the character embeddings, each word embedding being a multidimensional representation of a respective one of the words of the text sequence, the character sequence model comprising a first recurrent neural network that receives the character embeddings for a word of the text sequence and outputs the word representation for the word; generating a sequence representation for the text sequence with a trained word sequence model, based on the word representations; generating at least one trait prediction with a trained trait model, based on the sequence representation; and outputting the trait prediction or information based on the trait prediction, wherein at least one of the generating character embeddings, generating word representations, generating the sequence representation, and generating at least one trait prediction is performed with a processor.

1. A method for trait prediction comprising: receiving a text sequence comprising words, each of the words including at least one character, some of the words comprising more than one character; generating a character embeddings for each of the characters of the text sequence, each character embedding being a multidimensional representation of a respective one of the characters; generating word representations for words of the text sequence with a trained character sequence model, based on the character embeddings, each word embedding being a multidimensional representation of a respective one of the words of the text sequence, the character sequence model comprising a first recurrent neural network that receives the character embeddings for a word of the text sequence and outputs the word representation for the word; generating a sequence representation for the text sequence with a trained word sequence model, based on the word representations; generating at least one trait prediction with a trained trait model, based on the sequence representation; and outputting the trait prediction or information based on the trait prediction, wherein at least one of the generating character embeddings, generating word representations, generating the sequence representation, and generating at least one trait prediction is performed with a processor. 12. The method of claim 1 , wherein the generating of the at least one trait prediction comprises generating a trait prediction for an author of a group of text sequences, based on respective sequence representations of the text sequences.

1. A method for representing list information in a markup language document, comprising: at a computing device, internally representing an application document in a word-processing application, wherein the internal representation is in a non-markup language format that is native to the application and the internal representation comprises unique properties for describing lists of data within the document, wherein the unique properties are defined by the application; at the computing device, determining one or more unique properties corresponding to a list that relates to at least one section of the application document; at the computing device, mapping the determined properties of the list into at least one of a markup language element, an attribute, and/or a value; and at the computing device, storing the mapped properties of the list in the markup language document, wherein the markup language document is manipulable on a system including one of a server and another system to substantially reproduce the list without using the application that generated the markup language document.

1. A method for representing list information in a markup language document, comprising: at a computing device, internally representing an application document in a word-processing application, wherein the internal representation is in a non-markup language format that is native to the application and the internal representation comprises unique properties for describing lists of data within the document, wherein the unique properties are defined by the application; at the computing device, determining one or more unique properties corresponding to a list that relates to at least one section of the application document; at the computing device, mapping the determined properties of the list into at least one of a markup language element, an attribute, and/or a value; and at the computing device, storing the mapped properties of the list in the markup language document, wherein the markup language document is manipulable on a system including one of a server and another system to substantially reproduce the list without using the application that generated the markup language document. 2. The method of claim 1 , further comprising determining whether the list is a picture bulleted list.

1. A computer-implemented method, comprising: generating, by a computing system, declarative script models with services using a Data Notation Architecture (DNA), wherein the DNA provides a structure to identify and modify data schemas; creating instructions on how corresponding target application code is to be generated, by the computing system, using a Resolution Notation Architecture (RNA), wherein a given RNA file categorically qualifies and defines how DNA base pairs are resolved in the corresponding target application code; and generating rendered code and markup files for a corresponding target application as a part of a Genetic layer to be executed by a computing system associated with the corresponding target application using the RNA to create precompiled RNA.

1. A computer-implemented method, comprising: generating, by a computing system, declarative script models with services using a Data Notation Architecture (DNA), wherein the DNA provides a structure to identify and modify data schemas; creating instructions on how corresponding target application code is to be generated, by the computing system, using a Resolution Notation Architecture (RNA), wherein a given RNA file categorically qualifies and defines how DNA base pairs are resolved in the corresponding target application code; and generating rendered code and markup files for a corresponding target application as a part of a Genetic layer to be executed by a computing system associated with the corresponding target application using the RNA to create precompiled RNA. 8. The computer-implemented method of claim 1 , wherein when processing an RNA file, DNA files are related through the base pairs, the RNA file qualifies which base pair can be used to generate a given code file resource, and each code file resource represents a model, service, or visual resource.

11. A system for identifying phrasal terms, the system comprising: a processor; and a processor-readable storage medium in communication with the processor, wherein the processor-readable storage medium contains one or more programming instructions for performing a method of determining lexical association for phrasal terms, the method comprising: receiving a text having a plurality of words; determining, via the processor, a plurality of contexts, wherein a context comprises one or more words proximate to another word in the text, the plurality of contexts including contexts with differing lengths, wherein some of the contexts have a length of a number of words and some other contexts have a length of a different number of words; for each context, determining, via the processor, a first frequency based on a number of occurrences of the context within the text; assigning, via the processor, a first rank to at least one context based on the first frequency for the at least one context; determining multiple word-context pairs, and for each word-context pair, determining, via the processor, a second frequency based on a number of occurrences of the word associated with the word-context pair being used in the context; assigning, via the processor, a second rank to at least one word-context pair based on the second frequency for the at least one word-context pair; determining, via the processor, a rank ratio for each word-context pair equal to the first rank divided by the second rank; determining, via the processor, a mutual rank ratio based on multiple rank ratios, the multiple rank ratios including rank ratios associated with contexts of differing lengths; normalizing, via the processor, the mutual rank ratio to account for the contexts of differing lengths; and identifying a phrasal term using the mutual rank ratio, the phrasal term being a multiword unit of a vocabulary.

11. A system for identifying phrasal terms, the system comprising: a processor; and a processor-readable storage medium in communication with the processor, wherein the processor-readable storage medium contains one or more programming instructions for performing a method of determining lexical association for phrasal terms, the method comprising: receiving a text having a plurality of words; determining, via the processor, a plurality of contexts, wherein a context comprises one or more words proximate to another word in the text, the plurality of contexts including contexts with differing lengths, wherein some of the contexts have a length of a number of words and some other contexts have a length of a different number of words; for each context, determining, via the processor, a first frequency based on a number of occurrences of the context within the text; assigning, via the processor, a first rank to at least one context based on the first frequency for the at least one context; determining multiple word-context pairs, and for each word-context pair, determining, via the processor, a second frequency based on a number of occurrences of the word associated with the word-context pair being used in the context; assigning, via the processor, a second rank to at least one word-context pair based on the second frequency for the at least one word-context pair; determining, via the processor, a rank ratio for each word-context pair equal to the first rank divided by the second rank; determining, via the processor, a mutual rank ratio based on multiple rank ratios, the multiple rank ratios including rank ratios associated with contexts of differing lengths; normalizing, via the processor, the mutual rank ratio to account for the contexts of differing lengths; and identifying a phrasal term using the mutual rank ratio, the phrasal term being a multiword unit of a vocabulary. 17. The system of claim 11 , wherein at least one of the contexts comprises a gap.

11. The system of claim 1 , wherein the Database of Attack-Identifying Rules comprises a network virus rule, the network virus rule being when a suspect program is running to detect that: none of its correlative programs have windows, the suspect program copies itself and performs system registry modifications to enable itself or its copy to run at system startup, and the suspect program executes actions comprising sending a data package, network listening, injecting a thread into other processes, creating a global hook and sending a mail.

11. The system of claim 1 , wherein the Database of Attack-Identifying Rules comprises a network virus rule, the network virus rule being when a suspect program is running to detect that: none of its correlative programs have windows, the suspect program copies itself and performs system registry modifications to enable itself or its copy to run at system startup, and the suspect program executes actions comprising sending a data package, network listening, injecting a thread into other processes, creating a global hook and sending a mail. 12. The system of claim 11 , wherein the Database of Attack-Identifying Rules comprises a worm virus rule, the worm virus rule being to detect: a suspect program, receives files from a mail system or an instant communication software, and after the suspect program runs, the suspect program operates a user keyboard and/or user mouse to simulates user's actions to automatically send mails by the mail system and/or to automatically send files via the instant communication software.

11. A system to configure an application server for XML applications, comprising: a delegation layer on an application server that is transparent to the XML applications, wherein at least one of the XML applications invokes one or more XML parsing operations, and wherein the delegation layer delegates the one or more XML parsing operations to one of a plurality of parsers that are associated with the delegation layer; and a re-parsing mechanism that is plugged into the delegation layer, and the re-parsing mechanism to perform, retrieving a local copy of a document type definition (DTD) file or a schema (XSD) file from a local cache of the application server; performing the one or more XML parsing operations based on the local copy; retrieving an updated copy of the document type definition (DTD) file or the schema (XSD) file from a remote application server, if an error is detected during the one or more XML parsing operations based on the local copy; and performing the one or more XML parsing operations again based on the updated copy.

11. A system to configure an application server for XML applications, comprising: a delegation layer on an application server that is transparent to the XML applications, wherein at least one of the XML applications invokes one or more XML parsing operations, and wherein the delegation layer delegates the one or more XML parsing operations to one of a plurality of parsers that are associated with the delegation layer; and a re-parsing mechanism that is plugged into the delegation layer, and the re-parsing mechanism to perform, retrieving a local copy of a document type definition (DTD) file or a schema (XSD) file from a local cache of the application server; performing the one or more XML parsing operations based on the local copy; retrieving an updated copy of the document type definition (DTD) file or the schema (XSD) file from a remote application server, if an error is detected during the one or more XML parsing operations based on the local copy; and performing the one or more XML parsing operations again based on the updated copy. 16. The system according to claim 11 , wherein: the re-parsing mechanism can further perform repeating multiple parsing processes.

11. A computing device, comprising: one or more processors; and a non-transitory computer-readable storage medium having a plurality of instructions stored thereon, which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: executing a communication application; receiving a received textual message from a sender user, the received textual message including text content; determining an insertion point for the received textual message based on the text content, the insertion point corresponding to a particular location of a plurality of possible locations in a graphical user interface of the communication application, each of the plurality of possible locations corresponding to a position in the graphical user interface subsequent to a preceding textual message; and displaying the received textual message in the graphical user interface of the communication application at the determined insertion point, wherein: the received textual message is received by the computing device at a first time; and when (i) the computing device is displaying the graphical user interface at the first time, and (ii) the determined insertion point corresponds to a re-ordered position other than a most recent textual message position, the displaying the received textual message in the graphical user interface at the determined insertion point comprises providing an active indication of the received textual message being inserted at the insertion point.

11. A computing device, comprising: one or more processors; and a non-transitory computer-readable storage medium having a plurality of instructions stored thereon, which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: executing a communication application; receiving a received textual message from a sender user, the received textual message including text content; determining an insertion point for the received textual message based on the text content, the insertion point corresponding to a particular location of a plurality of possible locations in a graphical user interface of the communication application, each of the plurality of possible locations corresponding to a position in the graphical user interface subsequent to a preceding textual message; and displaying the received textual message in the graphical user interface of the communication application at the determined insertion point, wherein: the received textual message is received by the computing device at a first time; and when (i) the computing device is displaying the graphical user interface at the first time, and (ii) the determined insertion point corresponds to a re-ordered position other than a most recent textual message position, the displaying the received textual message in the graphical user interface at the determined insertion point comprises providing an active indication of the received textual message being inserted at the insertion point. 12. The computing device of claim 11 , wherein when at least one of: (i) the computing device is not displaying the graphical user interface at the first time, and (ii) the determined insertion point does not correspond to the re-ordered position: the displaying the received textual message in the graphical user interface at the determined insertion point comprises not providing the active indication.

13. A computer program product tangibly embodied in a non-transitory computer-readable storage medium and comprising instructions that when executed by a processor perform a method for creating a mapping, the method comprising: identifying a first schema that includes a plurality of first data element definitions, each of the first data element definitions defining a semantic of a data portion in first electronic documents that are generated according to a format of the first schema, wherein each of the first data element definitions in the first schema is uniquely identified by a respective first name; receiving an indication that the first schema is to be mapped to a second schema, the first and second schemas being different from each other such that a computer system configured according to the second schema is unable to semantically interpret the first electronic documents, wherein a naming rule specifies a process to generate a name for a data element from a human-understandable description for the data element by performing linguistic analysis on the human-understandable description for the data element, wherein each of multiple second data element definitions in the second schema is uniquely identified by a respective second name generated using the naming rule, wherein the first names that identify the first data element definitions in the first schema are not generated using the naming rule; generating a new name for each of the first data element definitions from the human-understandable description for each of the first data element definitions by applying the process that is specified by the naming rule to the human-understandable description for each of the first data element definitions, wherein the second names and the new names are defined by Core Components Technical Specification (CCTS) standard, and wherein the first names are not defined by the CCTS standard; and mapping at least one of the first data element definitions in the first schema to a corresponding one of the second data element definitions in the second schema based on the new name for the one of the first data element definitions in the first schema matching the second name of the one of the second data element definition in the second schema.

13. A computer program product tangibly embodied in a non-transitory computer-readable storage medium and comprising instructions that when executed by a processor perform a method for creating a mapping, the method comprising: identifying a first schema that includes a plurality of first data element definitions, each of the first data element definitions defining a semantic of a data portion in first electronic documents that are generated according to a format of the first schema, wherein each of the first data element definitions in the first schema is uniquely identified by a respective first name; receiving an indication that the first schema is to be mapped to a second schema, the first and second schemas being different from each other such that a computer system configured according to the second schema is unable to semantically interpret the first electronic documents, wherein a naming rule specifies a process to generate a name for a data element from a human-understandable description for the data element by performing linguistic analysis on the human-understandable description for the data element, wherein each of multiple second data element definitions in the second schema is uniquely identified by a respective second name generated using the naming rule, wherein the first names that identify the first data element definitions in the first schema are not generated using the naming rule; generating a new name for each of the first data element definitions from the human-understandable description for each of the first data element definitions by applying the process that is specified by the naming rule to the human-understandable description for each of the first data element definitions, wherein the second names and the new names are defined by Core Components Technical Specification (CCTS) standard, and wherein the first names are not defined by the CCTS standard; and mapping at least one of the first data element definitions in the first schema to a corresponding one of the second data element definitions in the second schema based on the new name for the one of the first data element definitions in the first schema matching the second name of the one of the second data element definition in the second schema. 17. The computer program product of claim 13 , wherein generating the new name for each first data element definition comprises: receiving a human-understandable description of a specific first data element definition for which a new name is to be created, the new name complying with a predefined name format that is same as a predefined name format of the second names; identifying a noun phrase and a verb phrase in the human-understandable description; and generating the new name using a first noun obtained from the noun phrase and a second noun obtained from the verb phrase.

11. A non-transitory machine-readable storage device, comprising instructions which, responsive to being executed by a processor, cause the processor to perform operations comprising: receiving, from a computing device, a voice message that is associated with media content; determining an identity of the media content, wherein the determining is based on metadata provided with the media content, on an analysis of the media content or on a combination thereof; determining, based on the identity of the media content, a context of the voice message; recognizing a word in the voice message, wherein the recognizing is based on the context of the voice message; generating text representing the voice message; obtaining additional content, wherein the obtaining is based on the identity of the media content and on the context of the voice message; generating an enhanced message, wherein the enhanced message comprises the text and the additional content; and providing a communication device with access to the enhanced message, wherein the communication device and the computing device are associated with each other by a social network.

11. A non-transitory machine-readable storage device, comprising instructions which, responsive to being executed by a processor, cause the processor to perform operations comprising: receiving, from a computing device, a voice message that is associated with media content; determining an identity of the media content, wherein the determining is based on metadata provided with the media content, on an analysis of the media content or on a combination thereof; determining, based on the identity of the media content, a context of the voice message; recognizing a word in the voice message, wherein the recognizing is based on the context of the voice message; generating text representing the voice message; obtaining additional content, wherein the obtaining is based on the identity of the media content and on the context of the voice message; generating an enhanced message, wherein the enhanced message comprises the text and the additional content; and providing a communication device with access to the enhanced message, wherein the communication device and the computing device are associated with each other by a social network. 12. The machine-readable storage device of claim 11 , wherein the recognizing further comprises selecting a speech recognition grammar, wherein the selecting is in accordance with the context of the voice message, and wherein the text is generated according to the speech recognition grammar.

1. A method for a computer apparatus for automatically generating a customer interaction log for an interaction between a customer and an agent, the method comprising: automatically analyzing received input corresponding to an interaction in the form of a call transcript between the customer and the agent to generate a customer interaction log using at least one model; displaying the customer interaction log for review by the agent at a graphical user interface of an agent computer; enabling the agent to provide feedback associated with the displayed generated customer interaction log by way of the graphical user interface; and automatically updating, based on the agent feedback, at least the customer interaction log.

1. A method for a computer apparatus for automatically generating a customer interaction log for an interaction between a customer and an agent, the method comprising: automatically analyzing received input corresponding to an interaction in the form of a call transcript between the customer and the agent to generate a customer interaction log using at least one model; displaying the customer interaction log for review by the agent at a graphical user interface of an agent computer; enabling the agent to provide feedback associated with the displayed generated customer interaction log by way of the graphical user interface; and automatically updating, based on the agent feedback, at least the customer interaction log. 11. The method of claim 1 further comprising generating the customer interaction log with reference to a realtime model automatically generated for the interaction.

11. The computer-implemented method of claim 10 wherein rendering information comprises rendering a plurality of options to the user as a function of the data in accordance with the input that has been received.

11. The computer-implemented method of claim 10 wherein rendering information comprises rendering a plurality of options to the user as a function of the data in accordance with the input that has been received. 13. The computer-implemented method of claim 11 wherein rendering information comprises rendering visual indications to the user.

14. A non-transitory computer readable storage medium storing one or more programs configured for execution by one or more processors of a system for sharing digital resources of a publisher through a web service, the one or more programs comprising instructions to be executed by the one or more processors so as to: receive a selection from a publishing user of a digital resource to share, wherein the digital resource is associated with a plurality of views; receive a selection from the publishing user of a group of subscribing users, the selection indicating that the digital resource is to be shared with the group of subscribing users according to one of the plurality of views; receive a selection of a sharing relationship from the publishing user, the sharing relationship specifying allowable interactions with the selected digital resource by the group of subscribing users; and in accordance with the selection of the sharing relationship, control interactions of the selected group of subscribing users with the one of the plurality of views of the selected digital resource, including at least: in accordance with a determination that the selected sharing relationship includes a first sharing relationship, sending a copy of the one of the plurality of views of the digital resource to the selected group of subscribing users, wherein the one of the plurality views has metadata describing a sharing style that corresponds to the selected sharing relationship for the selected group of subscribing users; and in response to receiving from the publishing user a change notification concerning modification to the selected digital resource: forwarding the change notification to the selected group of subscribing users, receiving from a subset of the selected group of subscribing users a request to block future notifications from the publishing user, and modifying the sharing relationship to enable blocking of future notifications from the publishing user.

14. A non-transitory computer readable storage medium storing one or more programs configured for execution by one or more processors of a system for sharing digital resources of a publisher through a web service, the one or more programs comprising instructions to be executed by the one or more processors so as to: receive a selection from a publishing user of a digital resource to share, wherein the digital resource is associated with a plurality of views; receive a selection from the publishing user of a group of subscribing users, the selection indicating that the digital resource is to be shared with the group of subscribing users according to one of the plurality of views; receive a selection of a sharing relationship from the publishing user, the sharing relationship specifying allowable interactions with the selected digital resource by the group of subscribing users; and in accordance with the selection of the sharing relationship, control interactions of the selected group of subscribing users with the one of the plurality of views of the selected digital resource, including at least: in accordance with a determination that the selected sharing relationship includes a first sharing relationship, sending a copy of the one of the plurality of views of the digital resource to the selected group of subscribing users, wherein the one of the plurality views has metadata describing a sharing style that corresponds to the selected sharing relationship for the selected group of subscribing users; and in response to receiving from the publishing user a change notification concerning modification to the selected digital resource: forwarding the change notification to the selected group of subscribing users, receiving from a subset of the selected group of subscribing users a request to block future notifications from the publishing user, and modifying the sharing relationship to enable blocking of future notifications from the publishing user. 18. The computer readable storage medium of claim 14 , wherein: a user that requests the digital resource can edit information in a local copy of the digital resource, and the edited information in the local copy of the digital resource is overwritten if the publishing user updates the digital resource.

1. A computer implemented educational training method, the method comprising: generating an educational training exercise at a particular skill level, the educational training exercise being an exercise to teach a user in reading and literacy; generating a user interface for the educational training exercise at the particular skill level wherein the user interface for the educational training exercise at the particular skill level is displayed on a computing device to the user; generating an instructional flow for the educational training exercise at the particular skill level wherein the instructional flow includes a plurality of teaching and testing portions; providing an early bail out trigger during a teaching or testing portion in an early stage of the instructional flow that is triggered by a processor when poor performance by the user is identified during the teaching or testing portion in the early stage of the instructional flow, and automatically returns the user to immediately restart the educational training exercise at the particular skill level; and providing an early regression trigger during a teaching or testing portion in a later stage of the instructional flow that is triggered by a processor when, after passing the early bail out trigger, poor performance by the user is identified during the teaching or testing portion in the later stage of the instructional flow, and automatically returns the user to immediately restart the educational training exercise at a less challenging or reduced skill level.

1. A computer implemented educational training method, the method comprising: generating an educational training exercise at a particular skill level, the educational training exercise being an exercise to teach a user in reading and literacy; generating a user interface for the educational training exercise at the particular skill level wherein the user interface for the educational training exercise at the particular skill level is displayed on a computing device to the user; generating an instructional flow for the educational training exercise at the particular skill level wherein the instructional flow includes a plurality of teaching and testing portions; providing an early bail out trigger during a teaching or testing portion in an early stage of the instructional flow that is triggered by a processor when poor performance by the user is identified during the teaching or testing portion in the early stage of the instructional flow, and automatically returns the user to immediately restart the educational training exercise at the particular skill level; and providing an early regression trigger during a teaching or testing portion in a later stage of the instructional flow that is triggered by a processor when, after passing the early bail out trigger, poor performance by the user is identified during the teaching or testing portion in the later stage of the instructional flow, and automatically returns the user to immediately restart the educational training exercise at a less challenging or reduced skill level. 2. The method of claim 1 further comprising automatically returning the user to immediately restart the educational training exercise at a less challenging or reduced skill level, if the early bail out trigger is again triggered when, after restarting the education training exercise at the particular skill level, poor performance by the user is again identified during the early stage of the instructional flow for the educational training exercise.

1. A method of providing a document service according to a service level agreement, the method involving a device comprising a hardware processor, and the method comprising: executing, by the processor, instructions that cause the device to; for each different and distinct respective service feature of at least two service features of the document service, provide at least two candidate components wherein each candidate component comprises a different set of service feature capabilities than other candidate components; and fulfill a request to provide the document service according to the service level agreement by selecting a particular service component for each respective service feature of the at least two service features of the document service, wherein the selecting the particular service component comprising: identifying a capability for the service feature that is specified by the service level agreement for the document service, and among the candidate components for the service feature, identifying a service component that provides the service feature with the capability that is specified by the service level agreement for the service feature; and composing the selected particular service components for the respective at least two service features to provide the document service.

1. A method of providing a document service according to a service level agreement, the method involving a device comprising a hardware processor, and the method comprising: executing, by the processor, instructions that cause the device to; for each different and distinct respective service feature of at least two service features of the document service, provide at least two candidate components wherein each candidate component comprises a different set of service feature capabilities than other candidate components; and fulfill a request to provide the document service according to the service level agreement by selecting a particular service component for each respective service feature of the at least two service features of the document service, wherein the selecting the particular service component comprising: identifying a capability for the service feature that is specified by the service level agreement for the document service, and among the candidate components for the service feature, identifying a service component that provides the service feature with the capability that is specified by the service level agreement for the service feature; and composing the selected particular service components for the respective at least two service features to provide the document service. 3. The method of claim 1 , wherein: the at least two service features of the document service including an indexing interface provided by at least two indexing components respectively configured to index documents of the document service according to a document indexing technique exhibiting at least one indexing characteristic; and the selecting the particular service component for the indexing interface comprising: selecting a indexing component implementing a document indexing technique configured to index the documents of the document service according to the document indexing technique exhibiting the at least one indexing characteristics satisfying at least one indexing criterion specified by the service level agreement.

12. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving an input term in a first language format; identifying two different groups of hyperlinks that each link to a same plurality of intermediary documents, wherein the different groups of hyperlinks have anchor texts in different respective language formats, including: identifying a first group of hyperlinks each having a respective first anchor text that includes the input term in the first language format, and identifying a second group of hyperlinks each having a respective second anchor text in a second language format; determining, from all of the second anchor texts of the second group of hyperlinks, a second term in the second language format that corresponds to the input term in the first language format, including: computing a total count of terms, including duplicates, occurring in all of the second anchor texts of the second group of hyperlinks, computing a respective individual count of occurrences, in all of the second anchor texts of the second group of hyperlinks of each of a plurality of terms in the second language format that occur in all of the second anchor texts of the second group of hyperlinks, computing a respective score for each of the plurality of terms in the second language format that occur in all of the second anchor texts of the second group of hyperlinks, including comparing, for each term of the plurality of terms in the second language format that occur in all of the second anchor texts of the second group of hyperlinks, the respective individual count for the term to the total count of terms occurring in all of the second anchor texts of the second group of hyperlinks, and designating a highest-scoring term among the plurality of terms in the second language format that occur in all of the second anchor texts of the second group of hyperlinks as the second term in the second language format that corresponds to the input term in the first language format; receiving a first query having the input term in the first language format; generating a revised query that includes the second term in the second language format; obtaining search results using the revised query; and providing the search results obtained using the revised query in response to receiving the first query.

12. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving an input term in a first language format; identifying two different groups of hyperlinks that each link to a same plurality of intermediary documents, wherein the different groups of hyperlinks have anchor texts in different respective language formats, including: identifying a first group of hyperlinks each having a respective first anchor text that includes the input term in the first language format, and identifying a second group of hyperlinks each having a respective second anchor text in a second language format; determining, from all of the second anchor texts of the second group of hyperlinks, a second term in the second language format that corresponds to the input term in the first language format, including: computing a total count of terms, including duplicates, occurring in all of the second anchor texts of the second group of hyperlinks, computing a respective individual count of occurrences, in all of the second anchor texts of the second group of hyperlinks of each of a plurality of terms in the second language format that occur in all of the second anchor texts of the second group of hyperlinks, computing a respective score for each of the plurality of terms in the second language format that occur in all of the second anchor texts of the second group of hyperlinks, including comparing, for each term of the plurality of terms in the second language format that occur in all of the second anchor texts of the second group of hyperlinks, the respective individual count for the term to the total count of terms occurring in all of the second anchor texts of the second group of hyperlinks, and designating a highest-scoring term among the plurality of terms in the second language format that occur in all of the second anchor texts of the second group of hyperlinks as the second term in the second language format that corresponds to the input term in the first language format; receiving a first query having the input term in the first language format; generating a revised query that includes the second term in the second language format; obtaining search results using the revised query; and providing the search results obtained using the revised query in response to receiving the first query. 16. The system of claim 12 , wherein the documents comprise web pages.

1. A method comprising: receiving, by a processor, an email message addressed to an email account; receiving, by the processor, a user request for display of the received email message; configuring, by the processor, the display of the received email message to comprise at least one user interface element that facilitates a user to request for other messages in the email account that are contextually relevant to the received email message; transmitting, by the processor, the received email message and the user interface element for display to the user; receiving, by the processor, a user activation of the user interface element; identifying, by the processor, at least one message in the user's email account that is contextually relevant to the received email message, comprising: constructing, by the processor, vectors representing the received email message and each of the other messages in the email account from the weights of relevant keywords extracted from the received message; ranking, by the processor, the plurality of clusters in a descending order of relevance based on respective ones of the relevant keywords associated with each of the plurality of clusters; selecting, by the processor, from the plurality of ranked clusters, a first ranked cluster as most relevant to the received email message; and ranking, by the processor, messages within the first ranked cluster based on respective similarities to the received message; automatically recommending, by the processor, search terms other than the relevant keywords, that are relevant to the received email message; and transmitting, by the processor, the at least one other contextually relevant message for display to the user.

1. A method comprising: receiving, by a processor, an email message addressed to an email account; receiving, by the processor, a user request for display of the received email message; configuring, by the processor, the display of the received email message to comprise at least one user interface element that facilitates a user to request for other messages in the email account that are contextually relevant to the received email message; transmitting, by the processor, the received email message and the user interface element for display to the user; receiving, by the processor, a user activation of the user interface element; identifying, by the processor, at least one message in the user's email account that is contextually relevant to the received email message, comprising: constructing, by the processor, vectors representing the received email message and each of the other messages in the email account from the weights of relevant keywords extracted from the received message; ranking, by the processor, the plurality of clusters in a descending order of relevance based on respective ones of the relevant keywords associated with each of the plurality of clusters; selecting, by the processor, from the plurality of ranked clusters, a first ranked cluster as most relevant to the received email message; and ranking, by the processor, messages within the first ranked cluster based on respective similarities to the received message; automatically recommending, by the processor, search terms other than the relevant keywords, that are relevant to the received email message; and transmitting, by the processor, the at least one other contextually relevant message for display to the user. 4. The method of claim 1 , wherein the other relevant message comprises at least one message from a different thread than a thread comprising the received email message.

14. A computer-readable storage containing instructions for causing a computing device to carry out a method of building a language model, comprising: providing a first language model derived from a first corpus comprising a first set of data files, wherein each of the first set of data files is associated with a different set of text elements; providing a second language model derived from a second corpus different from the first corpus comprising a second set of data files, wherein i) each of the second set of data files is associated with a different set of text elements, ii) each of the data files in the first set of data files corresponds to a respective data file in the second set of data files, and iii) a data file in the first set of data files corresponds to a data file in the second set of data files if the data file in the first set of data files is associated with a similar set of text elements as is associated with the data file in the second set of data files; and merging, in parallel, respective data files in the first set of data files with corresponding data files in the second set of data files, thereby generating a combined language model by merging the first language model with the second language model.

14. A computer-readable storage containing instructions for causing a computing device to carry out a method of building a language model, comprising: providing a first language model derived from a first corpus comprising a first set of data files, wherein each of the first set of data files is associated with a different set of text elements; providing a second language model derived from a second corpus different from the first corpus comprising a second set of data files, wherein i) each of the second set of data files is associated with a different set of text elements, ii) each of the data files in the first set of data files corresponds to a respective data file in the second set of data files, and iii) a data file in the first set of data files corresponds to a data file in the second set of data files if the data file in the first set of data files is associated with a similar set of text elements as is associated with the data file in the second set of data files; and merging, in parallel, respective data files in the first set of data files with corresponding data files in the second set of data files, thereby generating a combined language model by merging the first language model with the second language model. 23. The computer-readable storage of claim 14 , wherein merging respective data files of the first set of data files with corresponding data files of the second set of data files results in a set of merged data files.

1. A computer implemented method comprising: a) receiving by a computer comprising a processor and a memory a set of original templates and storing the set of original templates in the memory; b) accessing by a computer a set of databases comprising a large corpus of documents and searching by a search engine the set of databases based on the set of original templates; c) identifying by the search engine a set of candidate sentences from a set of documents in the corpus by using a similarity measure to determine a similarity score, wherein the similarity measure comprises extracting a first set of tokens from at least one template from the set of original templates and extracting a second set of tokens from at least one candidate sentence from the set of candidate sentences, the first set of tokens and the second set of tokens each comprising a set of token-level 1 to token-level n grams, and further comprises comparing the extracted first set of tokens with the extracted second set of tokens by determining a first value representing an intersection of the extracted first and second sets of tokens, and dividing that first value by a second value derived by applying a minimum function to the extracted first and second sets of tokens to determine the similarity score; d) automatically eliminating candidate sentences from the set of candidate sentences based upon a similarity score threshold to arrive at a reduced set of candidate sentences determined to be syntactically similar to the at least one template; and e) processing the reduced set of candidate sentences to generate a set of natural language generation templates that, when processed by a computer and combined with a set of determined words or phrases, generate natural language text.

1. A computer implemented method comprising: a) receiving by a computer comprising a processor and a memory a set of original templates and storing the set of original templates in the memory; b) accessing by a computer a set of databases comprising a large corpus of documents and searching by a search engine the set of databases based on the set of original templates; c) identifying by the search engine a set of candidate sentences from a set of documents in the corpus by using a similarity measure to determine a similarity score, wherein the similarity measure comprises extracting a first set of tokens from at least one template from the set of original templates and extracting a second set of tokens from at least one candidate sentence from the set of candidate sentences, the first set of tokens and the second set of tokens each comprising a set of token-level 1 to token-level n grams, and further comprises comparing the extracted first set of tokens with the extracted second set of tokens by determining a first value representing an intersection of the extracted first and second sets of tokens, and dividing that first value by a second value derived by applying a minimum function to the extracted first and second sets of tokens to determine the similarity score; d) automatically eliminating candidate sentences from the set of candidate sentences based upon a similarity score threshold to arrive at a reduced set of candidate sentences determined to be syntactically similar to the at least one template; and e) processing the reduced set of candidate sentences to generate a set of natural language generation templates that, when processed by a computer and combined with a set of determined words or phrases, generate natural language text. 3. The method of claim 1 further comprising identifying all sentences in the corpus by splitting each sentence from each other sentence for every document in the corpus.

15. The computer of claim 1 further comprising a controller unit operatively coupled with the events details records unit and configured to process said events details records and to select one or more actions if a result of said processing matches a predefined criterion.

15. The computer of claim 1 further comprising a controller unit operatively coupled with the events details records unit and configured to process said events details records and to select one or more actions if a result of said processing matches a predefined criterion. 16. The computer of claim 15 further comprising an output manager configured to communicate with the controller unit and to enable generating the performance in accordance with one or more actions selected by the controller unit and communicated to the output manager.

14. A computer-readable medium encoded with a computer program product for managing federated search results as recited in claim 12 includes the steps of creating a graphical representation for each of the selected search engines.

14. A computer-readable medium encoded with a computer program product for managing federated search results as recited in claim 12 includes the steps of creating a graphical representation for each of the selected search engines. 15. A computer-readable medium encoded with a computer program product for managing federated search results as recited in claim 14 includes the steps of adding priority statistics to each said graphical representation for each of the selected search engines.

1. A computer-implemented method, comprising: receiving a user input into a user interface of an input method editor (IME); determining, based on the user input, whether to process the user input with a script engine; when the user input indicates that the user input is to be processed with the script engine: providing the user input to the script engine, selecting a script from a plurality of scripts electronically stored in a script repository, processing the user input through the script using the script engine to generate one or more candidates, and providing the one or more candidates to an IME engine; when the user input indicates that the user input is not to be processed with the script engine: providing the user input to the IME engine, and processing the user input with the IME engine to generate the one or more candidates; and receiving an extension mode input indicating operation of the IME in an extension mode, operating the IME in the extension mode in response to receiving the extension mode input, and providing all user input to the script engine when operating in the extension mode.

1. A computer-implemented method, comprising: receiving a user input into a user interface of an input method editor (IME); determining, based on the user input, whether to process the user input with a script engine; when the user input indicates that the user input is to be processed with the script engine: providing the user input to the script engine, selecting a script from a plurality of scripts electronically stored in a script repository, processing the user input through the script using the script engine to generate one or more candidates, and providing the one or more candidates to an IME engine; when the user input indicates that the user input is not to be processed with the script engine: providing the user input to the IME engine, and processing the user input with the IME engine to generate the one or more candidates; and receiving an extension mode input indicating operation of the IME in an extension mode, operating the IME in the extension mode in response to receiving the extension mode input, and providing all user input to the script engine when operating in the extension mode. 8. The computer-implemented method of claim 1 , wherein the user input comprises the extension mode input.

21. A computer readable medium having instructions stored thereon that, when executed by one or more processors, causes the one or more processors to perform operations comprising: obtaining a plurality of search results responsive to a search query submitted by a user, wherein each search result refers to a respective document that is associated with a respective plurality of click measures, each click measure relating to a different respective natural language and representing, at least, a measure of behavior of users associated with the respective language in regards to the document when the document was referred to in a search result previously provided in response to the search query; for each of a first plurality of the search results, reducing the click measure associated with the document referred to by the search result, wherein the click measure relates to a respective natural language that is incompatible with a natural language of the user; calculating a respective scoring factor for each of the first plurality of search results based on the respective click measures associated with the document referred to by the first search result; and ranking the search results based upon, at least, the calculated scoring factors.

21. A computer readable medium having instructions stored thereon that, when executed by one or more processors, causes the one or more processors to perform operations comprising: obtaining a plurality of search results responsive to a search query submitted by a user, wherein each search result refers to a respective document that is associated with a respective plurality of click measures, each click measure relating to a different respective natural language and representing, at least, a measure of behavior of users associated with the respective language in regards to the document when the document was referred to in a search result previously provided in response to the search query; for each of a first plurality of the search results, reducing the click measure associated with the document referred to by the search result, wherein the click measure relates to a respective natural language that is incompatible with a natural language of the user; calculating a respective scoring factor for each of the first plurality of search results based on the respective click measures associated with the document referred to by the first search result; and ranking the search results based upon, at least, the calculated scoring factors. 30. The computer readable medium of claim 21 wherein the operations further comprise determining that the respective natural language is incompatible with the natural language of the user based on, at least, analysis of search queries submitted by users in different countries.

6. A method for updating a voice template for recognizing a speaker on the basis of a voice uttered by the speaker, the method comprising: storing a plurality of voice templates, each voice template indicating distinctive characteristics uttered by a corresponding one of a plurality of speakers; extracting distinctive characteristics uttered by a specific speaker on the basis of a voice message uttered by the specific speaker; selecting a voice template from the stored plurality of voice templates on the basis of a degree of similarity between the distinctive characteristics indicated by the selected voice template and the extracted distinctive characteristics; updating the selected specific voice template on the basis of the extracted distinctive characteristics; performing voice recognition on the voice message uttered by the specific speaker; extracting a keyword from specific data obtained as a result of the voice recognition; and determining a plurality of candidate templates on the basis of the extracted keyword, and wherein selecting the voice template comprises selecting the voice template from among from the plurality of candidate templates.

6. A method for updating a voice template for recognizing a speaker on the basis of a voice uttered by the speaker, the method comprising: storing a plurality of voice templates, each voice template indicating distinctive characteristics uttered by a corresponding one of a plurality of speakers; extracting distinctive characteristics uttered by a specific speaker on the basis of a voice message uttered by the specific speaker; selecting a voice template from the stored plurality of voice templates on the basis of a degree of similarity between the distinctive characteristics indicated by the selected voice template and the extracted distinctive characteristics; updating the selected specific voice template on the basis of the extracted distinctive characteristics; performing voice recognition on the voice message uttered by the specific speaker; extracting a keyword from specific data obtained as a result of the voice recognition; and determining a plurality of candidate templates on the basis of the extracted keyword, and wherein selecting the voice template comprises selecting the voice template from among from the plurality of candidate templates. 8. The method of claim 6 , further comprising: identifying an addressee of the voice message uttered by the specific speaker; and determining a plurality of candidate templates on the basis of a history of exchange of voice messages of the addressee; and wherein selecting the voice template comprises selecting the voice template from among from the plurality of candidate templates.

1. In a computing environment, a processor-implemented method of analyzing dynamic source code, the method comprising: parsing source code to generate one or more syntax trees defining constructs in a body of dynamic language source code; from the one or more syntax trees, extracting identifier information for one or more of the defined constructs; augmenting knowledge about the constructs by at least one of explicit inspection of the body of source code or implied references related to the source code; producing a correlation between identifiers and augmented knowledge; using the identifier information and augmented knowledge, generating metadata about the body of the dynamic language source code, the generated metadata being represented as a symbol table; parsing symbol table to compute metrics about the one or more syntax trees; and providing the metrics about the one or more syntax trees to a user, wherein the metrics provide code correctness analysis, and wherein the correctness analysis indicates parameter count mismatches.

1. In a computing environment, a processor-implemented method of analyzing dynamic source code, the method comprising: parsing source code to generate one or more syntax trees defining constructs in a body of dynamic language source code; from the one or more syntax trees, extracting identifier information for one or more of the defined constructs; augmenting knowledge about the constructs by at least one of explicit inspection of the body of source code or implied references related to the source code; producing a correlation between identifiers and augmented knowledge; using the identifier information and augmented knowledge, generating metadata about the body of the dynamic language source code, the generated metadata being represented as a symbol table; parsing symbol table to compute metrics about the one or more syntax trees; and providing the metrics about the one or more syntax trees to a user, wherein the metrics provide code correctness analysis, and wherein the correctness analysis indicates parameter count mismatches. 10. The method of claim 1 , wherein the correctness analysis indicates return completeness issues by indicating at least one of codepaths returning values or codepaths not returning values.

2. A computer-implemented method comprising: storing one or more user profiles of users of a social networking system and a set of connections among the users, each user profile including information corresponding to at least one user interest, and wherein one or more of the user profiles includes a connection to a brand page in the social networking system, the brand page associated with an entity, the information of each user profile including at least one affinity corresponding to the at least one user interest; receiving, at the social networking system, a request from a viewing user associated with a corresponding user profile to view the brand page stored in the social networking system; retrieving one or more keywords associated with a plurality of content items posted to the brand page, at least one of the plurality of content items including an additional content message for accessing one or more additional content items, and another at least one of the plurality of content items including at least one of: information about the entity associated with the brand page, information about a brand of the brand page, and information about a product associated with a brand of the brand page, and the one or more keywords describing at least one of the brand of the brand page and the information about the product associated with the brand of the brand page, and each of the one or more keywords defined by the entity; retrieving the user profile associated with the viewing user and maintained by the social networking system; selecting, by the social networking system, content items from the plurality of content items based on: an affinity of the at least one affinity, the affinity between at least one user interest and the content items, the one or more keywords associated with each of the plurality of content items, and the information corresponding to the at least one user interest in the user profile associated with the viewing user, wherein at least one of the selected content items includes the additional content message for accessing additional content items; and presenting the selected content items to the viewing user.

2. A computer-implemented method comprising: storing one or more user profiles of users of a social networking system and a set of connections among the users, each user profile including information corresponding to at least one user interest, and wherein one or more of the user profiles includes a connection to a brand page in the social networking system, the brand page associated with an entity, the information of each user profile including at least one affinity corresponding to the at least one user interest; receiving, at the social networking system, a request from a viewing user associated with a corresponding user profile to view the brand page stored in the social networking system; retrieving one or more keywords associated with a plurality of content items posted to the brand page, at least one of the plurality of content items including an additional content message for accessing one or more additional content items, and another at least one of the plurality of content items including at least one of: information about the entity associated with the brand page, information about a brand of the brand page, and information about a product associated with a brand of the brand page, and the one or more keywords describing at least one of the brand of the brand page and the information about the product associated with the brand of the brand page, and each of the one or more keywords defined by the entity; retrieving the user profile associated with the viewing user and maintained by the social networking system; selecting, by the social networking system, content items from the plurality of content items based on: an affinity of the at least one affinity, the affinity between at least one user interest and the content items, the one or more keywords associated with each of the plurality of content items, and the information corresponding to the at least one user interest in the user profile associated with the viewing user, wherein at least one of the selected content items includes the additional content message for accessing additional content items; and presenting the selected content items to the viewing user. 9. The computer-implemented method of claim 2 , wherein selecting content items from the plurality of content items comprises: matching a characteristic of the user profile associated with the viewing user with the at least one keyword associated with a content item of the plurality of content items.

13. A system for intelligent automation of computer software test scripts and code requirements, the system comprising a server computing device configured to automatically scan a plurality of code files stored in a code repository that comprise one or more lines of application source code to identify changes made to the code files, including: determining whether a timestamp of each of the code files has changed, and extracting one or more data elements associated with the code file and storing the data elements in a database, if the timestamp of the code file has changed, wherein the changes made to the code files include one or more of: changes to a folder structure in which the code file is located, changes to a configuration file of an application associated with the code file, changes to one or more lines of application source code contained in the code file, and changes to a build file of an application in which the code file is included; select one or more test automation script files from a test script repository that are related to the changed code files; parse each selected test automation script file to determine whether the selected test automation script file includes changes that correspond to the changes made to the related code files, including: extracting one or more data elements associated with the selected test automation script file and storing the data elements in the database, wherein the changes to the selected test automation script file include one or more of: changes to a folder structure in which the code file is located, and changes to one or more lines of script code contained in the selected test automation script file; if the selected test automation script file includes the corresponding changes: determine whether a current version of the selected test automation script file is located on each of one or more test server computing devices; and install the current version of the selected test automation script file on each test server computing device that does not have the current version; and if the selected test automation script file does not include the corresponding changes: transmit a notification message to a remote computing device to indicate that the selected test automation script file requires the corresponding changes.

13. A system for intelligent automation of computer software test scripts and code requirements, the system comprising a server computing device configured to automatically scan a plurality of code files stored in a code repository that comprise one or more lines of application source code to identify changes made to the code files, including: determining whether a timestamp of each of the code files has changed, and extracting one or more data elements associated with the code file and storing the data elements in a database, if the timestamp of the code file has changed, wherein the changes made to the code files include one or more of: changes to a folder structure in which the code file is located, changes to a configuration file of an application associated with the code file, changes to one or more lines of application source code contained in the code file, and changes to a build file of an application in which the code file is included; select one or more test automation script files from a test script repository that are related to the changed code files; parse each selected test automation script file to determine whether the selected test automation script file includes changes that correspond to the changes made to the related code files, including: extracting one or more data elements associated with the selected test automation script file and storing the data elements in the database, wherein the changes to the selected test automation script file include one or more of: changes to a folder structure in which the code file is located, and changes to one or more lines of script code contained in the selected test automation script file; if the selected test automation script file includes the corresponding changes: determine whether a current version of the selected test automation script file is located on each of one or more test server computing devices; and install the current version of the selected test automation script file on each test server computing device that does not have the current version; and if the selected test automation script file does not include the corresponding changes: transmit a notification message to a remote computing device to indicate that the selected test automation script file requires the corresponding changes. 24. The system of claim 13 , wherein the server computing device is further configured to receive one or more keywords associated with a code change requirement from an application lifecycle management system; identify at least one code file and at least one test automation script file based upon the keywords; and link the identified code file and the identified test automation script file to a user interface of the application lifecycle management system.

7. A non-transitory computer-readable medium that stores a computer program for providing domain-specific modules for domain-specific searches, the computer program including instructions that, when executed by at least one computer, cause the at least one computer to perform operations comprising: specifying one or more input elements and one or more output elements for a domain-specific search in a domain, the domain including data that relates the one or more input elements to the one or more output elements; identifying one or more related elements corresponding to metadata in the domain for the one or more input elements or output elements; determining a data set corresponding to the one or more input elements, output elements, and related elements in the domain; and using the data set to train and test a domain-specific module that relates the one or more input elements, output elements and related elements, the domain-specific module operating to receive input values for the one or more input elements and provide output values for the one or more output elements, and the output elements identifying a relevant document related to the domain, the relevant document corresponding to a domain-specific search result for a search defined by the input values.

7. A non-transitory computer-readable medium that stores a computer program for providing domain-specific modules for domain-specific searches, the computer program including instructions that, when executed by at least one computer, cause the at least one computer to perform operations comprising: specifying one or more input elements and one or more output elements for a domain-specific search in a domain, the domain including data that relates the one or more input elements to the one or more output elements; identifying one or more related elements corresponding to metadata in the domain for the one or more input elements or output elements; determining a data set corresponding to the one or more input elements, output elements, and related elements in the domain; and using the data set to train and test a domain-specific module that relates the one or more input elements, output elements and related elements, the domain-specific module operating to receive input values for the one or more input elements and provide output values for the one or more output elements, and the output elements identifying a relevant document related to the domain, the relevant document corresponding to a domain-specific search result for a search defined by the input values. 8. The computer-readable medium of claim 7 , wherein the domain-specific module is a first domain-specific module and the computer program includes instructions that, when executed by the at least one computer, cause the at least one computer to perform operations comprising: using the data set to train and test a classification module that classifies a search request for operations by one of a plurality of domain-specific modules including the first domain-specific module, the classification module including an artificial neural network (ANN) that identifies a domain-specific module corresponding to the search request.

1. A method, comprising: analyzing, to determine one or more keywords, descriptive data of a media item; analyzing, by a computing device, the one or more keywords and selecting, based on the analyzing of the one or more keywords, a knowledge domain for the media item from a plurality of different knowledge domains; identifying, based on the knowledge domain, a plurality of attribute fields associated with the knowledge domain; identifying, from different combinations of media analysis technologies that correspond to the plurality of different knowledge domains and that are usable to analyze media, a combination of media analysis technologies corresponding to the knowledge domain; analyzing, after identifying the combination of media analysis technologies, the media item using the combination of media analysis technologies to determine values for the plurality of attribute fields; and segmenting the media item into a plurality of segments as a function of the values for the plurality of attribute fields by at least determining beginning and ending boundaries for the plurality of segments as a function of the values for the plurality of attribute fields.

1. A method, comprising: analyzing, to determine one or more keywords, descriptive data of a media item; analyzing, by a computing device, the one or more keywords and selecting, based on the analyzing of the one or more keywords, a knowledge domain for the media item from a plurality of different knowledge domains; identifying, based on the knowledge domain, a plurality of attribute fields associated with the knowledge domain; identifying, from different combinations of media analysis technologies that correspond to the plurality of different knowledge domains and that are usable to analyze media, a combination of media analysis technologies corresponding to the knowledge domain; analyzing, after identifying the combination of media analysis technologies, the media item using the combination of media analysis technologies to determine values for the plurality of attribute fields; and segmenting the media item into a plurality of segments as a function of the values for the plurality of attribute fields by at least determining beginning and ending boundaries for the plurality of segments as a function of the values for the plurality of attribute fields. 11. The method of claim 1 , further comprising: storing the values for the plurality of attribute fields to a database; and storing the beginning and the ending boundaries for the plurality of segments to the database.

12. A method for assisting an author, comprising: receiving an author's review of an item which includes text and an associated author's rating of the item on a predefined scale; parsing the text of the author's review of the item to identify opinion expressions in the input text; with a processor, generating an analysis of the text, based on the identified opinion expressions including computing an effective opinion of the text as a function of a measure of polarity associated with each of the identified opinion expressions, the measure of polarity being is based on the polarity measure associated with respective adjectival terms from a polar vocabulary that are in the opinion expressions, the polar vocabulary associating a polarity measure with each of a set of adjectival terms, the polarity measure being based on ratings of reviews in a corpus of reviews from which the respective adjectival term was extracted; comparing the effective opinion with the author's rating to determine whether the text and the author's rating are coherent; and generating a representation of the analysis for display on a user interface, the representation of the analysis including a representation of the effective opinion.

12. A method for assisting an author, comprising: receiving an author's review of an item which includes text and an associated author's rating of the item on a predefined scale; parsing the text of the author's review of the item to identify opinion expressions in the input text; with a processor, generating an analysis of the text, based on the identified opinion expressions including computing an effective opinion of the text as a function of a measure of polarity associated with each of the identified opinion expressions, the measure of polarity being is based on the polarity measure associated with respective adjectival terms from a polar vocabulary that are in the opinion expressions, the polar vocabulary associating a polarity measure with each of a set of adjectival terms, the polarity measure being based on ratings of reviews in a corpus of reviews from which the respective adjectival term was extracted; comparing the effective opinion with the author's rating to determine whether the text and the author's rating are coherent; and generating a representation of the analysis for display on a user interface, the representation of the analysis including a representation of the effective opinion. 17. The method of claim 12 , wherein in the representation, each of the identified opinion expressions is identified.

12. The method according to claim 4 , wherein step (d) comprises the steps of: (d1) determining a pattern of the morpheme-analyzed and tagged sentence; (d2) recognizing a parallel structure, dividing the sentence by parallel node parsing, and performing syntax node parsing, when determined to be the syntax pattern; (d3) performing syntax node parsing for each node of the sentence pattern, when determined to be the sentence pattern; and (d4) treating the syntax node parsed result as one chart, parsing the whole sentence again, and generating a final structure analysis result.

12. The method according to claim 4 , wherein step (d) comprises the steps of: (d1) determining a pattern of the morpheme-analyzed and tagged sentence; (d2) recognizing a parallel structure, dividing the sentence by parallel node parsing, and performing syntax node parsing, when determined to be the syntax pattern; (d3) performing syntax node parsing for each node of the sentence pattern, when determined to be the sentence pattern; and (d4) treating the syntax node parsed result as one chart, parsing the whole sentence again, and generating a final structure analysis result. 13. The method according to claim 12 , wherein the parallel structure recognition comprises the steps of: generating a parallel structure candidate by the syntax pattern, when a partial sentence to be syntax node parsed is longer than a specific length; and selecting a parallel structure by a parallel node recognition means and a syntax node limitation with respect to each candidate of the parallel structure.

1. A natural language analysis method for an electronic device storing a basic corpus and a temporary corpus, the basic corpus recording a vast amount of vocabularies, words and phrases and a frequency of use of each word and each phrase, the temporary corpus recording the mapping relationship between at least one substitute vocabulary and at least one temporary meaning, the method comprising: generating signals in response to a user's input; converting the signals into a textualized message in a predetermined language; segmenting the textualized message into at least one vocabulary, and obtaining at least one vocabularized segment comprising the at least one vocabulary; segmenting the textualized message based on the vocabularized segments and a sentence construction rule, and obtaining at least one sentence segment; retrieving the frequency of use of each segmented vocabulary from the basic corpus, obtaining a first language understanding result of the textualized message by analyzing the at least one vocabularized segments and the at least one sentence segments, based on the retrieved frequency of use of each segmented vocabulary; selecting a plurality of textualized messages consecutively converted within a predetermined time period, the selected textualized messages including said textualized message which is segmented in the segmenting step; analyzing of the selected textualized messages using a contextual understanding method, so as to determine whether the first language understanding result is a reasonable understanding; if the first language understanding result is an unreasonable understanding, determining at least one substitute vocabulary causing the unreasonable understanding, determining whether the temporary corpus records the determined at least one substitute vocabulary; if the temporary corpus does not have a record of one or more of the determined at least one substitute vocabulary, determining temporary meaning of each determined substitute vocabulary by analyzing the context comprising the determined substitute vocabulary to obtain a second understanding result of the textualized message, and updating the temporary corpus by storing the mapping relationship between the determined at least one substitute vocabulary and the temporary meaning therein; and determining a reply message for the textualized message based on a reasonable language understanding result, the temporary corpus, and the basic corpus.

1. A natural language analysis method for an electronic device storing a basic corpus and a temporary corpus, the basic corpus recording a vast amount of vocabularies, words and phrases and a frequency of use of each word and each phrase, the temporary corpus recording the mapping relationship between at least one substitute vocabulary and at least one temporary meaning, the method comprising: generating signals in response to a user's input; converting the signals into a textualized message in a predetermined language; segmenting the textualized message into at least one vocabulary, and obtaining at least one vocabularized segment comprising the at least one vocabulary; segmenting the textualized message based on the vocabularized segments and a sentence construction rule, and obtaining at least one sentence segment; retrieving the frequency of use of each segmented vocabulary from the basic corpus, obtaining a first language understanding result of the textualized message by analyzing the at least one vocabularized segments and the at least one sentence segments, based on the retrieved frequency of use of each segmented vocabulary; selecting a plurality of textualized messages consecutively converted within a predetermined time period, the selected textualized messages including said textualized message which is segmented in the segmenting step; analyzing of the selected textualized messages using a contextual understanding method, so as to determine whether the first language understanding result is a reasonable understanding; if the first language understanding result is an unreasonable understanding, determining at least one substitute vocabulary causing the unreasonable understanding, determining whether the temporary corpus records the determined at least one substitute vocabulary; if the temporary corpus does not have a record of one or more of the determined at least one substitute vocabulary, determining temporary meaning of each determined substitute vocabulary by analyzing the context comprising the determined substitute vocabulary to obtain a second understanding result of the textualized message, and updating the temporary corpus by storing the mapping relationship between the determined at least one substitute vocabulary and the temporary meaning therein; and determining a reply message for the textualized message based on a reasonable language understanding result, the temporary corpus, and the basic corpus. 6. The method as described in claim 1 , further comprising: determining whether current conversation between the electronic device and the user is over; and deleting the temporary corpus when the current conversation is over.

30. The system of claim 23 , further comprising: assigning each of the plurality of document-query pairs to one of a plurality of second partitions according to a measure of popularity of the respective query of the document-query pair, each second partition being defined by a distinct respective measure of popularity range.

30. The system of claim 23 , further comprising: assigning each of the plurality of document-query pairs to one of a plurality of second partitions according to a measure of popularity of the respective query of the document-query pair, each second partition being defined by a distinct respective measure of popularity range. 31. The system of claim 30 , wherein determining the indication of quality of the website further comprises: aggregating the respective user feedback data associated with the document-query pairs in two or more of the second partitions; determining a distribution of aggregated user feedback data among the two or more second partitions; and determining the indication of quality of the website based on a combination of the first distribution and the second distribution.

30. A document data processing method for document retrieval according to claim 29, wherein in association with said concatenated component character table a bit list in which one-bit information are allocated to all usable character strings each composed of at least two characters, respectively, is prepared for each of said documents and wherein bit positions in said bit list for the character strings used in the documents are set to "1s", respectively, while the bit positions for the character strings not used in the documents are set to "0s", respectively.

30. A document data processing method for document retrieval according to claim 29, wherein in association with said concatenated component character table a bit list in which one-bit information are allocated to all usable character strings each composed of at least two characters, respectively, is prepared for each of said documents and wherein bit positions in said bit list for the character strings used in the documents are set to "1s", respectively, while the bit positions for the character strings not used in the documents are set to "0s", respectively. 36. A document data processing method for document retrieval according to claim 30, said concatenated component character table being prepared on the basis of the character strings each composed of n characters, wherein in the step of the concatenated component character table search, the document containing all the character strings each composed of n characters and contained without duplication in the search term designated by the operator is extracted by searching the bit list having the relevant bit positions all set to 1".

11. A non-transitory computer-readable storage medium having computer program instructions embodied therein for labeling context slices of a storyline of a user's movements, the computer program instructions comprising instructions for: receiving a plurality of context slices derived from associated context data collected from a plurality of observation sources; and for each of the plurality of context slices: determining an uncertainty in a location of the slice based on a type of observation source from which the associated context data originated; identifying a set of candidate labels based on context data associated with the context slice, each candidate label matching the location of the slice within the determined uncertainty, the candidate labels each comprising semantic data describing the context data associated with the context slice, the semantic data selected from a group consisting of geography, venue, and activity; ranking the set of candidate labels by likelihood; applying one or more of the candidate labels to the context slice; and storing a correspondence between the applied one or more labels and the context slice.

11. A non-transitory computer-readable storage medium having computer program instructions embodied therein for labeling context slices of a storyline of a user's movements, the computer program instructions comprising instructions for: receiving a plurality of context slices derived from associated context data collected from a plurality of observation sources; and for each of the plurality of context slices: determining an uncertainty in a location of the slice based on a type of observation source from which the associated context data originated; identifying a set of candidate labels based on context data associated with the context slice, each candidate label matching the location of the slice within the determined uncertainty, the candidate labels each comprising semantic data describing the context data associated with the context slice, the semantic data selected from a group consisting of geography, venue, and activity; ranking the set of candidate labels by likelihood; applying one or more of the candidate labels to the context slice; and storing a correspondence between the applied one or more labels and the context slice. 19. The computer-readable storage medium of claim 11 , the instructions further comprising instructions for providing a plurality of candidate labels to a user for possible selection.

4. The method of claim 3 , wherein, for each primitive of the first plurality of primitives added to the concentric graph database before any of the second plurality of primitives, the respective second unique identifier associated with the primitive is equivalent to the respective first unique identifier associated with the primitive.

4. The method of claim 3 , wherein, for each primitive of the first plurality of primitives added to the concentric graph database before any of the second plurality of primitives, the respective second unique identifier associated with the primitive is equivalent to the respective first unique identifier associated with the primitive. 5. The method of claim 4 , wherein, for each primitive of the first plurality of primitives added to the concentric graph database after an earliest primitive in the second plurality of primitives, the respective second unique identifier for the first primitive is different from the respective first unique identifier for the first primitive.

25. A document classifier comprising: a plurality of classifier engines; means for generating a metric for each classifier engine; means for comparing said metrics; and means for classifying a document using output from one or more of said classifier engines in response to said means for comparing; wherein said means for generating a metric generates, for each classifier engine, a list of probabilities of said document being classified by each classifier engine into each one of a group of possible classes, and wherein said means for comparing sums said probabilities for each class and identifies the class with the largest sum of probabilities, wherein said means for generating a metric further generates a precision value for each classifier engine, and wherein said means for comparing identifies a highest precision value and a second highest precision value and determines if said highest precision value is greater than said second highest precision value by a predetermined amount, and wherein said means for classifying classifies said document using output from the classifier engine having said highest precision value to classify said document if said highest precision value is greater than said second highest precision value by a predetermined amount and classifies said document into the class with the largest sum of probabilities if said highest precision value is not greater than said second highest precision value by a predetermined amount.

25. A document classifier comprising: a plurality of classifier engines; means for generating a metric for each classifier engine; means for comparing said metrics; and means for classifying a document using output from one or more of said classifier engines in response to said means for comparing; wherein said means for generating a metric generates, for each classifier engine, a list of probabilities of said document being classified by each classifier engine into each one of a group of possible classes, and wherein said means for comparing sums said probabilities for each class and identifies the class with the largest sum of probabilities, wherein said means for generating a metric further generates a precision value for each classifier engine, and wherein said means for comparing identifies a highest precision value and a second highest precision value and determines if said highest precision value is greater than said second highest precision value by a predetermined amount, and wherein said means for classifying classifies said document using output from the classifier engine having said highest precision value to classify said document if said highest precision value is greater than said second highest precision value by a predetermined amount and classifies said document into the class with the largest sum of probabilities if said highest precision value is not greater than said second highest precision value by a predetermined amount. 26. The document classifier of claim 25 further comprising means for weighting said probabilities for each class.

1. A method performed by a handheld mobile communication terminal for offering a dictionary feature, the method comprising: displaying at least a portion of a text document on a display unit configured for the handheld mobile communication terminal; in response to at least one touch input for selecting a word, displaying an enlarged version of a part of the displayed text document and providing an indication of a selected word and a visual element for receiving a user request for dictionary information corresponding to the selected word among the part of the displayed text document, the visual element overlaying less than all of the displayed text document; in response to the user request for the dictionary information via the visual element, displaying a dictionary screen in place of the displayed text document so that the dictionary screen visually appears to be over the entire displayed text document, the dictionary screen showing the dictionary information corresponding to the selected word; and in response to a user request for termination of the dictionary screen, returning from the dictionary screen to a screen showing the displayed text document with the indication of the selected word remaining, wherein the enlarged version of the part of the displayed text document and the part of the displayed text document are both displayed at the same time.

1. A method performed by a handheld mobile communication terminal for offering a dictionary feature, the method comprising: displaying at least a portion of a text document on a display unit configured for the handheld mobile communication terminal; in response to at least one touch input for selecting a word, displaying an enlarged version of a part of the displayed text document and providing an indication of a selected word and a visual element for receiving a user request for dictionary information corresponding to the selected word among the part of the displayed text document, the visual element overlaying less than all of the displayed text document; in response to the user request for the dictionary information via the visual element, displaying a dictionary screen in place of the displayed text document so that the dictionary screen visually appears to be over the entire displayed text document, the dictionary screen showing the dictionary information corresponding to the selected word; and in response to a user request for termination of the dictionary screen, returning from the dictionary screen to a screen showing the displayed text document with the indication of the selected word remaining, wherein the enlarged version of the part of the displayed text document and the part of the displayed text document are both displayed at the same time. 7. The method of claim 1 , further comprising manipulating the selection of the word, in response to a drag input.

41. The non-transitory computer-readable storage medium of claim 30 , wherein a plurality of sets of pairs of mystery number regions interposing pair clues are combined together to form one or more different geometric patterns.

41. The non-transitory computer-readable storage medium of claim 30 , wherein a plurality of sets of pairs of mystery number regions interposing pair clues are combined together to form one or more different geometric patterns. 42. The non-transitory computer-readable storage medium of claim 41 , wherein the puzzle layout is filled in by receiving any missing mystery number value among the plurality of sets.

5. The non-transitory computer readable storage medium of claim 4 , further comprising instructions for receiving a trigger event with respect to the first function, and further comprising instructions for determining whether a valid instance of the output of the first function exists in a cache.

5. The non-transitory computer readable storage medium of claim 4 , further comprising instructions for receiving a trigger event with respect to the first function, and further comprising instructions for determining whether a valid instance of the output of the first function exists in a cache. 6. The non-transitory computer readable storage medium of claim 5 , further comprising instructions for retrieving the output from the cache.

1. A method for filtering search results comprising: displaying a plurality of content items, the content items including one or more content display objects corresponding to a search query, one or more primary content category objects corresponding to the search query, and one or more secondary content category objects; receiving a selection of a content item; modifying the search query based on the selected content item; and displaying a second plurality of content items, including at least one second content display object corresponding to the modified search query that is different from the one or more content display objects, and at least one additional content category object different from the one or more primary content category object and one or more secondary content category objects, wherein a content display object is associated with a plurality of formats for displaying information, the plurality of formats being associated with the content display object prior to receiving the search query, the plurality of formats associated with a content display object being based on content types of the information provided by the content display object; wherein the one or more content category objects and the one or more secondary category objects are displayed as nodes in a graph navigation display of content categories.

1. A method for filtering search results comprising: displaying a plurality of content items, the content items including one or more content display objects corresponding to a search query, one or more primary content category objects corresponding to the search query, and one or more secondary content category objects; receiving a selection of a content item; modifying the search query based on the selected content item; and displaying a second plurality of content items, including at least one second content display object corresponding to the modified search query that is different from the one or more content display objects, and at least one additional content category object different from the one or more primary content category object and one or more secondary content category objects, wherein a content display object is associated with a plurality of formats for displaying information, the plurality of formats being associated with the content display object prior to receiving the search query, the plurality of formats associated with a content display object being based on content types of the information provided by the content display object; wherein the one or more content category objects and the one or more secondary category objects are displayed as nodes in a graph navigation display of content categories. 8. The method of claim 1 , wherein the method further comprises preparing the one or more content display objects after receiving the search query, the preparation of the content display objects comprising: obtaining information to be presented within the content display object; formatting the obtained information using at least one format or application selected from the plurality of formats or applications associated with the content display object; displaying the formatted information.

1. A server, comprising: a processor; and a memory connected to the processor, the memory storing instructions executed by the processor to: receive a query statement, wherein the query statement is one of a plurality of distributed storage and distributed processing query statements with unique data access methods, wherein the query statement is received over a network from a client machine; form token components from the query statement; categorize each token component of the token components as one of a data component, a computational logic component or a control logic component; form modified token components from the token components in accordance with a policy; reconstruct the query statement with the modified token components and original computational logic and control logic associated with the query statement to form a policy compliant query statement; and coordinate execution of the policy compliant query statement on worker machines connected to the network.

1. A server, comprising: a processor; and a memory connected to the processor, the memory storing instructions executed by the processor to: receive a query statement, wherein the query statement is one of a plurality of distributed storage and distributed processing query statements with unique data access methods, wherein the query statement is received over a network from a client machine; form token components from the query statement; categorize each token component of the token components as one of a data component, a computational logic component or a control logic component; form modified token components from the token components in accordance with a policy; reconstruct the query statement with the modified token components and original computational logic and control logic associated with the query statement to form a policy compliant query statement; and coordinate execution of the policy compliant query statement on worker machines connected to the network. 5. The server of claim 1 wherein the logic components are categorized as computational logic components or control logic components.

1. An infusion pump assembly comprising: a reservoir assembly configured to contain an infusible fluid; a motor assembly configured to act upon the reservoir assembly and dispense at least a portion of the infusible fluid contained within the reservoir assembly; processing logic configured to control the motor assembly; wherein the processing logic includes: a primary microprocessor configured to execute one or more primary applications written in a first computer language; and a safety microprocessor configured to execute one or more safety applications written in a second computer language that is different than the first computer language; and a computer program product residing on a computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising: receiving, on the primary microprocessor executing one or more applications written in a first computer language, an initial command processable by the one or more applications written in the first computer language; converting the initial command into a modified command processable by one or more applications written in a second computer language; and providing the modified command to the safety microprocessor executing the one or more applications written in the second computer language.

1. An infusion pump assembly comprising: a reservoir assembly configured to contain an infusible fluid; a motor assembly configured to act upon the reservoir assembly and dispense at least a portion of the infusible fluid contained within the reservoir assembly; processing logic configured to control the motor assembly; wherein the processing logic includes: a primary microprocessor configured to execute one or more primary applications written in a first computer language; and a safety microprocessor configured to execute one or more safety applications written in a second computer language that is different than the first computer language; and a computer program product residing on a computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising: receiving, on the primary microprocessor executing one or more applications written in a first computer language, an initial command processable by the one or more applications written in the first computer language; converting the initial command into a modified command processable by one or more applications written in a second computer language; and providing the modified command to the safety microprocessor executing the one or more applications written in the second computer language. 13. The infusion pump assembly of claim 1 wherein the audio system is configured to provide an escalating alarm sequence in the event of a loss of a beacon signal, wherein the escalating alarm sequence includes at least a low-intensity alarm and a high-intensity alarm.

7. A computer program product for making non-shared linked documents in email messages accessible to recipients, comprising a computer readable storage medium storing computer program code, wherein the computer program code when executed on a computer, causes the computer to: upon initiation of sending of an email message by a user, automatically parse through the MIME to detect URL tags indicating that a linked document is contained within the email message, upon detection of a URL tag, check a prefix of the URL tag to determine if the linked document is accessible to a recipient, if the prefix indicates that the linked document is not accessible to the recipient, provide for selection of at least sending the linked document as an in-line document by converting the document into HTML format and embedding it into a multi part MIME message; and if the sender is sending the email message with a linked Notes email and upon determining that the recipient of the email was originally a recipient of the Notes email, converting a Notes Universal ID of the linked Notes email to a MIME id of the Notes email, wherein the MIME id of the linked Notes email provides the recipient with access to the linked Notes email message.

7. A computer program product for making non-shared linked documents in email messages accessible to recipients, comprising a computer readable storage medium storing computer program code, wherein the computer program code when executed on a computer, causes the computer to: upon initiation of sending of an email message by a user, automatically parse through the MIME to detect URL tags indicating that a linked document is contained within the email message, upon detection of a URL tag, check a prefix of the URL tag to determine if the linked document is accessible to a recipient, if the prefix indicates that the linked document is not accessible to the recipient, provide for selection of at least sending the linked document as an in-line document by converting the document into HTML format and embedding it into a multi part MIME message; and if the sender is sending the email message with a linked Notes email and upon determining that the recipient of the email was originally a recipient of the Notes email, converting a Notes Universal ID of the linked Notes email to a MIME id of the Notes email, wherein the MIME id of the linked Notes email provides the recipient with access to the linked Notes email message. 12. The computer program product of claim 7 , wherein if the prefix is set, by default, as not being accessible to the recipient, the computer readable medium causes the computer to automatically select an option of converting the linked document into HTML format and embedding it into a multi part MIME message.

1. A method for detecting a 3D anatomical object in a medical image volume, comprising: determining, by a processor, a constrained search range in the medical image volume based on a set of annotated training volumes; detecting position candidates in said constrained search range using a first trained classifier; generating position-orientation hypotheses from said position candidates based on orientation examples in said set of training volumes; detecting position-orientation candidates from said position-orientation hypotheses using a second classifier; generating similarity transformation hypotheses from said position-orientation candidates based on scale examples in said set of training volumes; detecting similarity transformation candidates from said similarity transformation hypotheses using a third trained classifier; and detecting said 3D anatomical object in the medical image volume based on at least one of said similarity transformation candidates.

1. A method for detecting a 3D anatomical object in a medical image volume, comprising: determining, by a processor, a constrained search range in the medical image volume based on a set of annotated training volumes; detecting position candidates in said constrained search range using a first trained classifier; generating position-orientation hypotheses from said position candidates based on orientation examples in said set of training volumes; detecting position-orientation candidates from said position-orientation hypotheses using a second classifier; generating similarity transformation hypotheses from said position-orientation candidates based on scale examples in said set of training volumes; detecting similarity transformation candidates from said similarity transformation hypotheses using a third trained classifier; and detecting said 3D anatomical object in the medical image volume based on at least one of said similarity transformation candidates. 9. The method of claim 1 , wherein said step of detecting said 3D anatomical object in the medical image volume based on at least one of said similarity transformation candidates comprises: detecting said 3D anatomical object as having a position, orientation, and scale of a similarity transformation candidate having a highest probability.

1. A method performed by data processing apparatus, the method comprising: identifying queries in query data; determining, in each of the queries, (i) an entity-descriptive portion that refers to an entity and (ii) a suffix; determining query counts of a number of times that the respective queries were submitted; for at least a particular query of the identified queries, distributing the query count for the particular query among multiple different entities by assigning, to each of the multiple different entities, a partial query count that is an estimate of a number of submissions of the particular query that refer to the entity; estimating, based at least in part on one or more of the partial query counts, an entity-level count of query submissions that include a particular suffix and are considered to refer to a first entity of the multiple different entities; determining that the first entity is a particular type of entity; determining a type-level count of the query submissions that include the particular suffix and are estimated to refer to entities of the particular type of entity; and assigning, based on the entity-level count and the type-level count, a score for the particular suffix with respect to the first entity.

1. A method performed by data processing apparatus, the method comprising: identifying queries in query data; determining, in each of the queries, (i) an entity-descriptive portion that refers to an entity and (ii) a suffix; determining query counts of a number of times that the respective queries were submitted; for at least a particular query of the identified queries, distributing the query count for the particular query among multiple different entities by assigning, to each of the multiple different entities, a partial query count that is an estimate of a number of submissions of the particular query that refer to the entity; estimating, based at least in part on one or more of the partial query counts, an entity-level count of query submissions that include a particular suffix and are considered to refer to a first entity of the multiple different entities; determining that the first entity is a particular type of entity; determining a type-level count of the query submissions that include the particular suffix and are estimated to refer to entities of the particular type of entity; and assigning, based on the entity-level count and the type-level count, a score for the particular suffix with respect to the first entity. 14. The method of claim 1 , further comprising: determining that the first entity is assigned to each of multiple different entity types; and determining a ranking of the multiple different entity types with respect to the first entity.

1. A method comprises: applying speech recognition by a hand-held device that includes a processor device and memory to an audio recording to generate a text file including as text in the file recognized speech from the audio recording and determine an elapsed time period from a reference time in the audio recording to text in the file of recognized speech; comparing by the hand-held device text of recognized speech to expected text; generating by the hand-held device a timing file that is stored on a computer-readable storage medium, the timing file comprising the elapsed time information for the expected text; rendering on a display device associated with the hand-held device, text; rendering on the display device, a menu that displays graphics of multiple characters each of which is associated with a different audio recording; receiving by the hand-held device an indication of user-selected text, the user-selected text corresponding to portions of the text that are rendered aloud from corresponding portions of the audio recording for a first user selected character; determining by the hand-held device an elapsed time in the audio recording by referencing the timing file associated with the user-selected text; and providing by the mobile device an audible output corresponding to the audio in the audio recording at the determined elapsed time in the audio recording for the user selected portions of text for narration with the corresponding portions of the audio recording.

1. A method comprises: applying speech recognition by a hand-held device that includes a processor device and memory to an audio recording to generate a text file including as text in the file recognized speech from the audio recording and determine an elapsed time period from a reference time in the audio recording to text in the file of recognized speech; comparing by the hand-held device text of recognized speech to expected text; generating by the hand-held device a timing file that is stored on a computer-readable storage medium, the timing file comprising the elapsed time information for the expected text; rendering on a display device associated with the hand-held device, text; rendering on the display device, a menu that displays graphics of multiple characters each of which is associated with a different audio recording; receiving by the hand-held device an indication of user-selected text, the user-selected text corresponding to portions of the text that are rendered aloud from corresponding portions of the audio recording for a first user selected character; determining by the hand-held device an elapsed time in the audio recording by referencing the timing file associated with the user-selected text; and providing by the mobile device an audible output corresponding to the audio in the audio recording at the determined elapsed time in the audio recording for the user selected portions of text for narration with the corresponding portions of the audio recording. 9. The method of claim 1 wherein the display device allows the user to indicate a point in the text through one or more of a cursor a stylus and finger on a touch screen.

7. A system for distributing a resume of a job seeker to career websites, the system comprising: a server computer connected to a network; a storage device operationally adapted to the server computer; a job seeker profile and resume database stored within the storage device; a user interface presented on a client computer, the client computer communicating with the server computer to capture a list of target career websites from the job seeker; and a plurality of resume blasting bots executing on the server computer, each of the plurality of resume blasting bots navigating to a career website within the list of target career websites without user interaction through the network, if a username and a password for the job seeker for the career website is known, the resume blasting bot logs onto the career website without user interaction using the username and the password; and if the username and the password for the job seeker on the career website is unknown, the resume blasting bot creates a default user account on the career website using a default username and a default password and the resume blasting bot logs onto the career website without user interaction using the default username and the default password and the resume blasting bot fills in fields on the career website using the job seeker profile and resume database without user interaction.

7. A system for distributing a resume of a job seeker to career websites, the system comprising: a server computer connected to a network; a storage device operationally adapted to the server computer; a job seeker profile and resume database stored within the storage device; a user interface presented on a client computer, the client computer communicating with the server computer to capture a list of target career websites from the job seeker; and a plurality of resume blasting bots executing on the server computer, each of the plurality of resume blasting bots navigating to a career website within the list of target career websites without user interaction through the network, if a username and a password for the job seeker for the career website is known, the resume blasting bot logs onto the career website without user interaction using the username and the password; and if the username and the password for the job seeker on the career website is unknown, the resume blasting bot creates a default user account on the career website using a default username and a default password and the resume blasting bot logs onto the career website without user interaction using the default username and the default password and the resume blasting bot fills in fields on the career website using the job seeker profile and resume database without user interaction. 8. The system of claim 7 , wherein the network is a world-wide-web.

10. A computer readable storage medium comprising program instructions, wherein when executed the program instructions are operable to: receive a memory specification of a design under test (DUT), wherein the memory specification comprises configuration information on one or more memories of the DUT; analyze the memory specification to identify one or more memories; for each identified memory, parse the memory specification to generate a respective script comprising a plurality of commands, wherein the plurality of commands are configured to perform a memory dump operation of the identified memory; generate a top-level script to perform a memory dump operation of a plurality of identified memories.

10. A computer readable storage medium comprising program instructions, wherein when executed the program instructions are operable to: receive a memory specification of a design under test (DUT), wherein the memory specification comprises configuration information on one or more memories of the DUT; analyze the memory specification to identify one or more memories; for each identified memory, parse the memory specification to generate a respective script comprising a plurality of commands, wherein the plurality of commands are configured to perform a memory dump operation of the identified memory; generate a top-level script to perform a memory dump operation of a plurality of identified memories. 13. The computer readable storage medium as recited in claim 10 , wherein when executed the program instructions are further operable to: utilize the top-level script to perform a memory dump operation; receive an output bitstream from the memory dump operation; and process the output bitstream to generate a human-readable report of the memory dump operation.

1. An image recognition apparatus for passively identifying individuals in a monitored area comprising: means for storing a first set of reference facial image signatures wherein each reference facial image signature in the first set corresponds to a predetermined one of said individuals and is formed from an initial image of a predetermined individual by a first facial recognition methodology; means for storing a second set of reference facial image signatures wherein each reference facial image signature in the second set corresponds to a predetermined one of said individuals and is formed from an initial image of a predetermined individual by a second facial recognition methodology which is different from the first facial recognition methodology; image capturing means for capturing video images of a monitored area; means for extracting a first current facial image signature from the video image by processing the video images and by utilizing the first facial recognition methodology and for providing a first set of identity-indicating scores by comparing the first current facial image signature to each reference facial image signature of the first set of reference facial image signatures; means for extracting a second current facial image signature from the video image by processing the video images and by utilizing the second facial recognition methodology and for providing a second set of identity-indicating scores by comparing the second current facial image signature to each reference facial image signature of the second set of reference facial image signatures; and, means for fusing the first and second sets of identity-indicating scores to form a set of composite identity-indicating scores from which individuals may be identified.

1. An image recognition apparatus for passively identifying individuals in a monitored area comprising: means for storing a first set of reference facial image signatures wherein each reference facial image signature in the first set corresponds to a predetermined one of said individuals and is formed from an initial image of a predetermined individual by a first facial recognition methodology; means for storing a second set of reference facial image signatures wherein each reference facial image signature in the second set corresponds to a predetermined one of said individuals and is formed from an initial image of a predetermined individual by a second facial recognition methodology which is different from the first facial recognition methodology; image capturing means for capturing video images of a monitored area; means for extracting a first current facial image signature from the video image by processing the video images and by utilizing the first facial recognition methodology and for providing a first set of identity-indicating scores by comparing the first current facial image signature to each reference facial image signature of the first set of reference facial image signatures; means for extracting a second current facial image signature from the video image by processing the video images and by utilizing the second facial recognition methodology and for providing a second set of identity-indicating scores by comparing the second current facial image signature to each reference facial image signature of the second set of reference facial image signatures; and, means for fusing the first and second sets of identity-indicating scores to form a set of composite identity-indicating scores from which individuals may be identified. 2. The image recognition apparatus of claim 1 wherein the first set of reference facial image signatures are reference template signatures and wherein the means for extracting a first current facial image signature comprises means for extracting a current image template signature and for providing the first set of identity-indicating scores by comparing the current image template signature to each of the reference template signatures.

7. An image forming method comprising: convening page description language to page description language corresponding to a plurality of areas; generating image data based on the convened page description language; retaining in a memory the convened page description language and the generated image data; comparing the retained page description language and the converted page description language; selecting the retained image data in case the converted page description language in response to the comparison and the retained page description language correspond with each other, and selecting the generated image data in case the converted page description language in response to the comparison and the retained page description language do not correspond with each other.

7. An image forming method comprising: convening page description language to page description language corresponding to a plurality of areas; generating image data based on the convened page description language; retaining in a memory the convened page description language and the generated image data; comparing the retained page description language and the converted page description language; selecting the retained image data in case the converted page description language in response to the comparison and the retained page description language correspond with each other, and selecting the generated image data in case the converted page description language in response to the comparison and the retained page description language do not correspond with each other. 9. The image forming method according to claim 7 , wherein the area includes an area where one page is divided in one direction.

9. The hybrid prediction computer system as set forth in claim 6 wherein obtaining an adjustment factor for said tree-structured statistical table comprises: making a classification decision based up on said second set of features; and indexing into said tree-structured statistical table using said classification.

9. The hybrid prediction computer system as set forth in claim 6 wherein obtaining an adjustment factor for said tree-structured statistical table comprises: making a classification decision based up on said second set of features; and indexing into said tree-structured statistical table using said classification. 10. The hybrid prediction computer system as set forth in claim 9 wherein making a classification decision comprises making a web page classification and making an advertisement classification.

1. A method for semantic attribution of a request, said method implemented by a processor of a computer system, said method comprising: said processor receiving source data statements for the request; said processor receiving a selection of a domain for the received source data statements; said processor semantically analyzing the received source data statements, said semantically analyzing comprising matching elements in the received source data statements to respective one or more entries in an ontology associated with the selected domain, wherein the ontology comprises items and relationships that define the selected domain, and wherein each element in the received source data statements is a word or a phrase; after said matching is performed for one matched element of the matched elements, said processor determining whether completeness is indicated for the one matched element; responsive to determining that completeness is indicated for the one matched element, said processor fulfilling completeness for the one matched element; said processor assigning the one or more entries to the matched elements, respectively, to annotate each matched element with a respective annotation consisting of the respective one or more entries; said processor saving the annotated elements with the respective annotations; and said processor using the annotations to generate a search query for the request.

1. A method for semantic attribution of a request, said method implemented by a processor of a computer system, said method comprising: said processor receiving source data statements for the request; said processor receiving a selection of a domain for the received source data statements; said processor semantically analyzing the received source data statements, said semantically analyzing comprising matching elements in the received source data statements to respective one or more entries in an ontology associated with the selected domain, wherein the ontology comprises items and relationships that define the selected domain, and wherein each element in the received source data statements is a word or a phrase; after said matching is performed for one matched element of the matched elements, said processor determining whether completeness is indicated for the one matched element; responsive to determining that completeness is indicated for the one matched element, said processor fulfilling completeness for the one matched element; said processor assigning the one or more entries to the matched elements, respectively, to annotate each matched element with a respective annotation consisting of the respective one or more entries; said processor saving the annotated elements with the respective annotations; and said processor using the annotations to generate a search query for the request. 5. The method of claim 1 , wherein the one or more entries of the annotation that annotates one matched element of the matched elements consists of a plurality of entries.

15. A system for transforming unstructured information into content in a uniform context, comprising: hardware logic implemented in a computer to perform operations, the operations comprising: with a federation service of the computer that presents a single view of source content repositories to a user: receiving a query specifying source content groups stored in a set of the source content repositories; running the query to retrieve metadata schemas of the source content groups, wherein each source content group has a metadata schema that describes a structure of metadata associated with the unstructured information in the source content group; extracting the unstructured information and metadata associated with the unstructured information from the set of the source content repositories; in response to user input, receiving selection of target content groups in another set of target content repositories; in response to receiving the selection of the target content groups, identifying metadata schemas of the target content groups, wherein each metadata schema describes a structure of metadata associated with the unstructured information in the target content group; creating a schema definition file including the retrieved metadata schemas of the source content groups and the identified metadata schemas of the target content groups; forwarding the unstructured information, metadata, and schema definition file to a transformation service of the computer; receiving, from the transformation service, transformed unstructured information and transformed metadata; and loading the transformed, unstructured information and the metadata into the set of the target content repositories.

15. A system for transforming unstructured information into content in a uniform context, comprising: hardware logic implemented in a computer to perform operations, the operations comprising: with a federation service of the computer that presents a single view of source content repositories to a user: receiving a query specifying source content groups stored in a set of the source content repositories; running the query to retrieve metadata schemas of the source content groups, wherein each source content group has a metadata schema that describes a structure of metadata associated with the unstructured information in the source content group; extracting the unstructured information and metadata associated with the unstructured information from the set of the source content repositories; in response to user input, receiving selection of target content groups in another set of target content repositories; in response to receiving the selection of the target content groups, identifying metadata schemas of the target content groups, wherein each metadata schema describes a structure of metadata associated with the unstructured information in the target content group; creating a schema definition file including the retrieved metadata schemas of the source content groups and the identified metadata schemas of the target content groups; forwarding the unstructured information, metadata, and schema definition file to a transformation service of the computer; receiving, from the transformation service, transformed unstructured information and transformed metadata; and loading the transformed, unstructured information and the metadata into the set of the target content repositories. 17. The system of claim 15 , wherein the operations further comprise: using a transformation service to perform custom mappings to map the retrieved metadata schemas of the source content groups to metadata schemas of the target content groups and to perform custom transformations on at least one of the unstructured information and the metadata.

6. The method of claim 5 wherein analyzing the one or more training texts comprises performing one or more lexical, part-of-speech, or parsing analyses on the training texts.

6. The method of claim 5 wherein analyzing the one or more training texts comprises performing one or more lexical, part-of-speech, or parsing analyses on the training texts. 7. The method of claim 6 , wherein the parsing analyses generates instance values for one or more informative parse rule names identifying specific grammatical constructions.

4. The method of claim 1 , wherein the engine logic comprises at least one rule set, wherein said at least one rule set comprises a plurality of rules.

4. The method of claim 1 , wherein the engine logic comprises at least one rule set, wherein said at least one rule set comprises a plurality of rules. 5. The method of claim 4 , wherein each rule includes a condition portion and an executable portion, wherein the condition portion describes a condition that defines whether the rule is applicable, and wherein the executable portion describes at least one action to be performed if the rule is applicable.

11. A method for recognizing handwritten characters using predetermined patterns, comprising: generating the plurality of predetermined patterns, wherein the plurality of predetermined patterns are generated by performing one or more feature extraction operations on the character sample utilizing a Gabor filter, wherein the plurality of predetermined patterns are stored in a memory; acquiring an online handwritten character; pre-processing the online handwritten character; extracting features of the online handwritten character, thereby determining a feature vector, wherein extracting features of the online handwritten character to determine the feature vector comprises: extracting directional features based on the direction of the character sample's points; generating directional pattern images based on the directional features; filtering the directional pattern images using a Gabor filter; and forming the feature vector based on the filtered directional pattern images; generating, using a statistical algorithm, one or more patterns for the online handwritten character based on the feature vector; classifying the online handwritten character, comprising determining a character corresponding to the online handwritten character based on the generated one or more patterns and the plurality of predetermined patterns.

11. A method for recognizing handwritten characters using predetermined patterns, comprising: generating the plurality of predetermined patterns, wherein the plurality of predetermined patterns are generated by performing one or more feature extraction operations on the character sample utilizing a Gabor filter, wherein the plurality of predetermined patterns are stored in a memory; acquiring an online handwritten character; pre-processing the online handwritten character; extracting features of the online handwritten character, thereby determining a feature vector, wherein extracting features of the online handwritten character to determine the feature vector comprises: extracting directional features based on the direction of the character sample's points; generating directional pattern images based on the directional features; filtering the directional pattern images using a Gabor filter; and forming the feature vector based on the filtered directional pattern images; generating, using a statistical algorithm, one or more patterns for the online handwritten character based on the feature vector; classifying the online handwritten character, comprising determining a character corresponding to the online handwritten character based on the generated one or more patterns and the plurality of predetermined patterns. 12. The method of claim 11 , wherein the plurality of predetermined patterns are generated based on a statistical character recognition method.

13. The storage medium of claim 11 , wherein the method further comprises: determining a security policy for the target entity; determining a privacy policy for the target entity; and providing the content item to the target entity based on the determined security policy and the privacy policy for the target entity.

13. The storage medium of claim 11 , wherein the method further comprises: determining a security policy for the target entity; determining a privacy policy for the target entity; and providing the content item to the target entity based on the determined security policy and the privacy policy for the target entity. 16. The storage medium of claim 13 , wherein determining the target entity involves: determining one or more historical contexts that matches the contextual information for the local user; and determining a target entity which has received content during one or more of the historical contexts.

18. A system comprising: a first data processing arrangement configured to provide a distributed computing service; a data storage arrangement containing an ontology specification and a semantic interpretation specification, wherein the ontology specification describes messages of the distributed computing service, and the semantic interpretation specification describes rules for semantically handling the messages, as specified in the ontology specification, used to interface with the distributed computing service; a second data processing arrangement having a rules engine adapted for providing processor executable procedures, the second data processing arrangement configured to: receive a request to interface with the distributed computing service; accessing the ontology specification from the data storage arrangement; access the semantic interpretation specification from the data storage arrangement; enter the semantic interpretation specification into the rules engine; obtain a set of procedures from the rules engine for interacting with the distributed service based on the semantic interpretation specification; and interface with the distributed computing service using the set of procedures, wherein the interfacing includes forming distributed computing service messages based on the ontology specification and forming a service bridge having a generic programmatic interface adapted to receive the request.

18. A system comprising: a first data processing arrangement configured to provide a distributed computing service; a data storage arrangement containing an ontology specification and a semantic interpretation specification, wherein the ontology specification describes messages of the distributed computing service, and the semantic interpretation specification describes rules for semantically handling the messages, as specified in the ontology specification, used to interface with the distributed computing service; a second data processing arrangement having a rules engine adapted for providing processor executable procedures, the second data processing arrangement configured to: receive a request to interface with the distributed computing service; accessing the ontology specification from the data storage arrangement; access the semantic interpretation specification from the data storage arrangement; enter the semantic interpretation specification into the rules engine; obtain a set of procedures from the rules engine for interacting with the distributed service based on the semantic interpretation specification; and interface with the distributed computing service using the set of procedures, wherein the interfacing includes forming distributed computing service messages based on the ontology specification and forming a service bridge having a generic programmatic interface adapted to receive the request. 20. The system of claim 18 , wherein the a data storage arrangement is adapted for providing the semantic interpretation specification via a network.

1. A computer-implemented method of providing user-subscribed sources for secure search, comprising: providing to a user a template for crawling a source, the template defining a location of and crawl settings for a target data repository source, the template not having specified security credentials for the source; allowing a user to subscribe to the source using the template; receiving user-specified security credentials from the user and applying the user-specified security credentials to an instance of the template to create a user-subscribed source; authenticating a crawler as the user on the source; crawling, using a processor associated with a computer system, the source as the user template with user-specified security credentials; indexing documents for the user during the crawling in an index; and stamping identification information for the user with each entry in the index such that the associated documents are only available for search in the index by the user.

1. A computer-implemented method of providing user-subscribed sources for secure search, comprising: providing to a user a template for crawling a source, the template defining a location of and crawl settings for a target data repository source, the template not having specified security credentials for the source; allowing a user to subscribe to the source using the template; receiving user-specified security credentials from the user and applying the user-specified security credentials to an instance of the template to create a user-subscribed source; authenticating a crawler as the user on the source; crawling, using a processor associated with a computer system, the source as the user template with user-specified security credentials; indexing documents for the user during the crawling in an index; and stamping identification information for the user with each entry in the index such that the associated documents are only available for search in the index by the user. 24. The method of claim 1 wherein the operations of providing, allowing, receiving, and inheriting are all performed using the processor.

3. The method of claim 1 , further comprising de-provisioning the TS account from use when the TS session is closed.

3. The method of claim 1 , further comprising de-provisioning the TS account from use when the TS session is closed. 8. The method of claim 3 , wherein de-provisioning the TS account from use comprises: querying the TS account information for TS accounts having a cleanup status; in response to detecting a TS account having the cleanup status, deleting a roaming profile of the TS account and deleting the TS account; in response to detecting a TS account having an in use status, determining whether a duration of the TS session for the TS account having the in use status has reached a deletion time span; and in response to determining that the duration of the TS session has reached the deletion time span, deleting a roaming profile of the TS account having the in use status and deleting the TS account having the in use status.

1. A chatting system for a virtual pet, comprising: a pet client device including a processor coupled to a memory storing instructions for execution by the processor receiving a natural language sentence of a pet master and sending the natural language sentence to a virtual pet server device including a processor coupled to a memory storing instructions for execution by the processor; the virtual pet server device forwarding the natural language sentence to a question and answer server device including a processor coupled to a memory storing instructions for execution by the processor, and return a natural language response to the pet client device; the question and answer server device processing natural language understanding on the natural language sentence; obtaining language characteristics of the pet master and save the language characteristics of the pet master into a pet master language information base; generating the natural language response according to a natural language understanding result and the saved language characteristics of the pet master; and returning the natural language response to the virtual pet server device; wherein the language characteristics of the pet master include response habits commonly used by a user for responding to a certain natural language; the question and answer server device further adjusting the language characteristics of the pet master according to the natural language understanding result, history chatting records of the pet master, and the saved language characteristics of the pet master, and save the adjusted language characteristics of the pet master into the pet master language information base.

1. A chatting system for a virtual pet, comprising: a pet client device including a processor coupled to a memory storing instructions for execution by the processor receiving a natural language sentence of a pet master and sending the natural language sentence to a virtual pet server device including a processor coupled to a memory storing instructions for execution by the processor; the virtual pet server device forwarding the natural language sentence to a question and answer server device including a processor coupled to a memory storing instructions for execution by the processor, and return a natural language response to the pet client device; the question and answer server device processing natural language understanding on the natural language sentence; obtaining language characteristics of the pet master and save the language characteristics of the pet master into a pet master language information base; generating the natural language response according to a natural language understanding result and the saved language characteristics of the pet master; and returning the natural language response to the virtual pet server device; wherein the language characteristics of the pet master include response habits commonly used by a user for responding to a certain natural language; the question and answer server device further adjusting the language characteristics of the pet master according to the natural language understanding result, history chatting records of the pet master, and the saved language characteristics of the pet master, and save the adjusted language characteristics of the pet master into the pet master language information base. 10. The chatting system according to claim 1 , the pet client device is set in an IM tool.

6. The computer-implemented method of claim 1 , wherein said plug-in requires a user login, and wherein said plug-in stores a session identifier each time a user logs in to said plug-in so that successive logins can be referenced and attributed to said user.

6. The computer-implemented method of claim 1 , wherein said plug-in requires a user login, and wherein said plug-in stores a session identifier each time a user logs in to said plug-in so that successive logins can be referenced and attributed to said user. 7. The computer-implemented method of claim 6 , wherein said field definition tool associates session identifier with said flags such that changes to said objects within said document having a field value associated with said object can be detected on a successive login.

11. A document processing apparatus with an inlet and an outlet comprising: a document transport system having opposed transport members for moving documents serially along a generally/straight line document path from the inlet to the outlet; an optical system located between the inlet and the outlet and having a pair of mirrors to direct the image from one side of the document to an aperture orthogonal to the document path; and, a removable camera module having a lens with an optical axis and film such that when the camera is attached to the apparatus, the lens is aligned with the aperture to receive the image from the optical system; and an endorser system located downstream of the optical system for placing a single imprint endorsement mark on the document.

11. A document processing apparatus with an inlet and an outlet comprising: a document transport system having opposed transport members for moving documents serially along a generally/straight line document path from the inlet to the outlet; an optical system located between the inlet and the outlet and having a pair of mirrors to direct the image from one side of the document to an aperture orthogonal to the document path; and, a removable camera module having a lens with an optical axis and film such that when the camera is attached to the apparatus, the lens is aligned with the aperture to receive the image from the optical system; and an endorser system located downstream of the optical system for placing a single imprint endorsement mark on the document. 12. The apparatus of claim 11 wherein the endorser system is movable with respect to the apparatus to permit placement of a single endorsement mark in a second location on the document,

4. A method as in claim 1 , further comprising: inserting a recycle function in the functional model to return one of a material, an energy flow or a signal to a predecessor function.

4. A method as in claim 1 , further comprising: inserting a recycle function in the functional model to return one of a material, an energy flow or a signal to a predecessor function. 7. A method as in claim 4 , wherein a location for inserting the recycle function in the functional model is manually selected.

16. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising: receiving, from a separate entity, of a request to generate a model, input data and a seed model; receiving a cost function associated with generation of the model, the cost function indicating accuracy and one of speed and memory usage, wherein the cost function is formulated as: Accscore( ASR ( Xi ))= f ( Xi )→word accuracy and speed where the following definitions apply: f min( Xi )=−1* (word accuracy−β*speed), speed=(CPU time)/(audio time), β=a weighting factor to speed that provides a tradeoff between accuracy and speed, Xi=a set of parameters that affect accuracy and speed, such as beam width, LM scale, MAP multiplier, maximum active arcs, duration scale; processing the input data based on the seed model and based on parameters that modify the accuracy and the one of speed and memory usage of the cost function, to yield an updated model; and outputting the updated model.

16. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising: receiving, from a separate entity, of a request to generate a model, input data and a seed model; receiving a cost function associated with generation of the model, the cost function indicating accuracy and one of speed and memory usage, wherein the cost function is formulated as: Accscore( ASR ( Xi ))= f ( Xi )→word accuracy and speed where the following definitions apply: f min( Xi )=−1* (word accuracy−β*speed), speed=(CPU time)/(audio time), β=a weighting factor to speed that provides a tradeoff between accuracy and speed, Xi=a set of parameters that affect accuracy and speed, such as beam width, LM scale, MAP multiplier, maximum active arcs, duration scale; processing the input data based on the seed model and based on parameters that modify the accuracy and the one of speed and memory usage of the cost function, to yield an updated model; and outputting the updated model. 19. The computer-readable storage device of claim 16 , wherein the parameters comprise one of beam width, grammar scale, word insertion penalty, maximum arc length, maximum number of arcs allowed at any point in time, and state duration.

10. A computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for automatically classifying documents based on topics and user inputs, the method comprising: receiving a set of documents which are classified as relating to a specific topic; producing an initial feature vector that corresponds to frequency of a term's occurrence in the set of documents; using the initial feature vector to classify another set of documents to produce an initial classified set of documents; receiving click information associated with a set of queries related to the specific topic, wherein the click information includes a click-through rate at which a query result is selected after being presented and a click duration indicating an amount of time during which the query result is accessed; using the click information to remove off-topic documents in the set of documents to obtain an updated set of documents, wherein a document is off-topic if the click-through rate or click duration associated with the document indicates the document is off-topic; determining an updated feature vector using the updated set of documents; and re-classifying the classified set of documents using the updated feature vector when the percentage of documents identified as off-topic exceeds a threshold which is greater than 0, otherwise retaining the initial classified set of documents.

10. A computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for automatically classifying documents based on topics and user inputs, the method comprising: receiving a set of documents which are classified as relating to a specific topic; producing an initial feature vector that corresponds to frequency of a term's occurrence in the set of documents; using the initial feature vector to classify another set of documents to produce an initial classified set of documents; receiving click information associated with a set of queries related to the specific topic, wherein the click information includes a click-through rate at which a query result is selected after being presented and a click duration indicating an amount of time during which the query result is accessed; using the click information to remove off-topic documents in the set of documents to obtain an updated set of documents, wherein a document is off-topic if the click-through rate or click duration associated with the document indicates the document is off-topic; determining an updated feature vector using the updated set of documents; and re-classifying the classified set of documents using the updated feature vector when the percentage of documents identified as off-topic exceeds a threshold which is greater than 0, otherwise retaining the initial classified set of documents. 13. The computer-readable storage medium of claim 10 , wherein the query results are generated by identifying queries that match documents in the set of documents.