Patent Publication Number: US-8977538-B2

Title: Constructing and analyzing a word graph

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of and priority to U.S. Provisional Patent Application Ser. No. 61/382,028 filed on Sep. 13, 2010, which application is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND OF THE INVENTION 
     Computer analysis of written texts is an extremely difficult endeavor. Grammar rules tend to have many exceptions, some of which follow their own rules and some of which are arbitrary. Therefore, computer analysis tends to ignore grammar and focus instead on character order. I.e., it is limited to text recognition which attempts to determine which characters form the text and then compiles a text document which includes the characters produced. 
     A problem with this approach is that characters that are similar to one another can be confused. In particular, the text recognition cannot differentiate between characters that have very little, or no, differences. For example uppercase “I” and 1 are often confused by text recognition software. This is because the text recognition software is simply looking at the character individually, and not in the proper context. 
     Text recognition can try to “anticipate” what a character should be. However, to do so it relies on grammar rules. However, as previously noted grammar rules may have significant numbers or exceptions. In addition, the author may have used incorrect grammar so any anticipation of character in context is necessarily limited by the high probability of introducing errors. 
     Even if the text characters are known, such as in text files for example, a grammatical analysis can be difficult. Authors are not constrained to write text which is grammatically correct. Alternatively, different authors or groups of authors may have different standards for what is grammatically correct. In addition, some text may make sense to human readers but is not capable of being interpreted by a computer. The text can include abbreviations, words that include numbers or have any other number of complicating factors. 
     Accordingly, there is a need in the art for a way to analyze text. Additionally, there is a need in the art for the text analysis to proceed whether or not the text is grammatically correct. Further, there is a need in the art for a way to anticipate text. In addition, there is a need in the art for the anticipated text to be available to assist in character recognition. Moreover, there is a need in the art for the anticipated text to be available to assist in the composition of text. 
     BRIEF SUMMARY OF SOME EXAMPLE EMBODIMENTS 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential characteristics of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. 
     One example embodiment includes a method for constructing a word graph. The method includes obtaining a subject text and dividing the subject text into one or more units. The method also includes dividing the units into one or more sub-units and recording each of the one or more sub-units. 
     Another example embodiment includes a method for producing a word graph. The method includes dividing a subject text into one or more sentences. The method also includes assigning an identifier to each of the one or more sentences. The identifier is unique for each of the one or more sentences and identifies the occurrence of each of the one or more sentences within the subject text. The method further includes dividing the each of the one or more sentences into one or more words. The method additionally includes processing the words. Processing the words includes identifying the first word in each of the one or more sentences in a first word index and determining the relative placement of each word in each of the one or more sentences. 
     Another example embodiment includes a word graph for storing a subject text, wherein the subject text is divided into sentences and the sentences are further divided into words. The word graph includes one or more nodes, wherein each of the one or more nodes includes a word from a subject text. The word graph also includes a repeat factor, wherein the repeat factor indicates the number of instances in which one or more repeated words have been encountered in a sentence of the subject text. The word graph further includes a sentence identifier, wherein the sentence identifier identifies the sentence in the subject text. The word graph additionally includes a link between two words. The two words include a first node storing a first word and a second node storing a second word, wherein the second word is the word immediately following the first word in at least one location in the subject text. The link points from the first node to the second node. 
     These and other objects and features of the present invention will become more fully apparent from the following description and appended claims, or may be learned by the practice of the invention as set forth hereinafter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       To further clarify various aspects of some example embodiments of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It is appreciated that these drawings depict only illustrative embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail through the use of the accompanying drawings in which: 
         FIG. 1  illustrates an example of a method for creating a word graph; 
         FIG. 2  illustrates an example of a method for processing a sub-unit; 
         FIG. 3  illustrates an example of a method for processing a sub-unit pair of a subject text; 
         FIG. 4A  illustrates an example of adding a first sub-unit of a unit to a word graph; 
         FIG. 4B  illustrates an example of adding the second sub-unit of the unit to the word graph; 
         FIG. 4C  illustrates an example of a complete unit in the word graph; 
         FIG. 5A  illustrates an example of adding a second unit to the word graph while in process; 
         FIG. 5B  illustrates an example of adding a complete second unit to the word graph; 
         FIG. 6  illustrates an example of adding a complete third unit to a word graph; and 
         FIG. 7  illustrates an example of a suitable computing environment in which the invention may be implemented. 
     
    
    
     DETAILED DESCRIPTION OF SOME EXAMPLE EMBODIMENTS 
     Reference will now be made to the figures wherein like structures will be provided with like reference designations. It is understood that the figures are diagrammatic and schematic representations of some embodiments of the invention, and are not limiting of the present invention, nor are they necessarily drawn to scale. 
       FIG. 1  illustrates an example of a method  100  for creating a word graph. In at least one implementation, the word graph can represent the sub-units from a set of natural language units or other syntactic units. The units can be in the English language or any other language. The word graph can preserve and reflect the order of the sub-units in the units at a unit-by-unit level. I.e., the word graph can include a data structure that is an innovative variation of a more general data structure commonly referred to as a “graph.” A unit of text is loaded into the word graph by parsing it one sub-unit at a time in the same order as a human reader would (left-to-right in many languages, such as English). 
     Once loaded with one or more units, the word graph contains information about the units, their constituent sub-units and various relationships between them that can be extracted and/or analyzed for any number of useful purposes. Some of these include the following. 
     Comparison of an Example Unit 
     Given a example unit (one not already loaded in the word graph, whether grammatically “correct” or not), the word graph can be iterated and analyzed to identify one or more units loaded in the word graph that most closely correspond to the example unit. In addition to tracking the total number of sub-units in common, the word graph allows for other comparison criteria. This includes a comparison that factors in the relative sub-unit order of common sub-units. Such a comparison can be performed on multiple loaded units, and a matching score assigned to each. Additionally or alternatively, the comparison and scoring process can be further refined by consideration of authorship and/or other source information of the one or more loaded units to which the example unit is compared. One way that this can be accomplished is through the assignment of a unique identifier to one or more units and maintenance of an index that associates each such unique identifier with authorship and/or other source information with respect to each of the one or more such units. 
     Text Search Using Example Unit Comparison 
     Example unit comparison can be used to perform a text search against the word graph to identify matching loaded units that might not otherwise be identified—or might be assigned a different or lower relevance or hit rate—through a search examining only search terms (without consideration of sub-unit order). This process can be performed either as a complete search in itself or as a second step applied to the results of a primary search-terms search. In the latter case, a word graph is loaded with units that are returned as hits in the first-step search. Additionally or alternatively, the search process can be further refined by consideration of authorship and/or other source information of the one or more loaded units to which the example unit is compared. One way that this can be accomplished is through the assignment of a unique identifier to one or more units and maintenance of an index that associates each such unique identifier with authorship and/or other source information with respect to each of the one or more such units. 
     Automated Proof-Reading Using Example Unit Comparison 
     Using example unit comparison, a example unit can be validated against a word graph loaded with units that are known to have already been adequately proof-read or otherwise reviewed or approved by an author, editor or other reviewer. The example unit can be compared to one or more loaded units that most closely match it (based on given match criteria). If the example unit matches one of these loaded units, then the example unit is ipso facto considered to be proof-read (given the premise that all units in the word graph are known to have already been adequately proof-read). If it does not, further comparison can be made to determine portions of the example unit that differ from the loaded units and may therefore be in error. This process can be repeated for each unit in a particular text to be proof-read. Additionally or alternatively, this process can be further refined by consideration of authorship and/or other source information of the one or more loaded units to which the example unit is compared. One example of this could be a situation where a particular unit might be considered to be in error for one author but not another author. I.e. the automated proofreading process can make allowance for the idiosyncrasies of one or more individual authors or groups of authors. One way that this can be accomplished is through the assignment of a unique identifier to one or more units and maintenance of an index that associates each such unique identifier with authorship and/or other source information with respect to each of the one or more such units. 
     Improved Blackline Comparison Using Example Unit Comparison 
     A computer blacklining text comparison program can use example unit comparison to more accurately identify units or other text regions in two or more different documents that are related. This can improve the tracking of subtle changes to a text over successive generations of edits. 
     Next Sub-Unit and Rest of Unit Prediction Using Example Unit Comparison 
     Using example unit comparison and given a example unit fragment, it is possible to analyze the word graph to determine which sub-unit is statistically most likely to follow (or a list of possible next sub-units with their respective statistical likelihoods). This process can then be repeated if desired to predict subsequent sub-units in addition to the immediately following one. Additionally or alternatively, this process can be further refined by consideration of authorship and/or other source information of the one or more loaded units to which the example unit is compared. One way that this can be accomplished is through the assignment of a unique identifier to one or more units and maintenance of an index that associates each such unique identifier with authorship and/or other source information with respect to each of the one or more such units. 
     Predictive Drafting Assistance Through Next Sub-Unit Prediction and Rest of Unit Prediction 
     Next sub-unit prediction and rest of unit prediction can be used to assist in auto-completion of a unit that an author is drafting in real time. As the author drafts the unit, next sub-unit prediction and rest of unit prediction can be used to suggest additional sub-unit(s) in real time. This prediction process can be made more accurate by using a word graph containing units previously drafted by this same author (assuming the author&#39;s style is consistent). 
     Increasing Accuracy of Speech Recognition Through Next Sub-Unit Prediction and Rest of Unit Prediction 
     Predictive drafting assistance can be used to increase the accuracy of speech recognition where a unit author is “drafting” a unit by dictating it aloud to a speech recognition computer program. In choosing among different possible sub-units that such a program might “think” it heard from the author, predictive drafting assistance can be used to determine which ones are statistically most likely. This prediction process can be made more accurate if the author style consistency principle obtains. 
       FIG. 1  shows that the method includes obtaining  102  a subject text. In at least one implementation, the subject text can be obtained  102  from any desired source. For example, the subject text can include existing text, such as books, newspapers, magazines, journals, articles, web pages or other printed or electronic text, or newly created text such as text being created in real time by a user. Additionally or alternatively, the subject text can include model text used for compiling proofread materials. 
       FIG. 1  also shows that the method  100  can include parsing  104  the subject text. In at least one implementation, parsing  104  the subject text can include dividing the subject text into any desired units. For example, parsing  104  the subject text can include dividing the text into chapters, sections, paragraphs, sentences, phrases, words, lines, class of words, phrase types or any other desired division. Class of words can include the word type, such as noun, verb, object, subject, article, preposition, etc. 
       FIG. 1  further shows that the method  100  can include determining  106  whether any units have been located. In at least one implementation, determining  106  whether any units have been located can include determining whether units remain to be placed in the word graph. I.e., the units can be placed in a queue or other location where they are stored until they are to be placed within the word graph. Additionally or alternatively, the units can be parsed and placed in the word graph until the end of the subject text. 
       FIG. 1  additionally shows that the method  100  can include processing  108  the current unit. In at least one implementation, processing  108  the current unit can include placing the current unit in the word graph. Additionally or alternatively, processing  108  the current unit can include analyzing the relationship of the current unit to other units. I.e., the placement of the current unit in the subject text relative to other units can be analyzed and stored as well, as described below. 
     One skilled in the art will appreciate that, for this and other processes and methods disclosed herein, the functions performed in the processes and methods may be implemented in differing order. Furthermore, the outlined steps and operations are only provided as examples, and some of the steps and operations may be optional, combined into fewer steps and operations, or expanded into additional steps and operations without detracting from the essence of the disclosed embodiments. 
       FIG. 2  illustrates an example of a method  200  for processing a unit. In at least one implementation, processing the unit can include determining the structure of the unit. In particular, processing the unit can include analyzing the relationship of constituent sub-units. The relationship of the sub-units can be preserved for analysis, as described above. 
       FIG. 2  shows that the method  200  includes assigning  202  an identifier to the unit. In at least one implementation, the identifier can be any label which identifies the unit. For example, the identifier can be a tag or number assigned to the unit. Additionally or alternatively, the identifier can include the memory address within a digital medium of the beginning character of the unit. 
     As used herein, the word unit shall mean a first portion of the subject text. One of skill in the art will appreciate that although a unit can include a sentence as commonly used (i.e., a grammatical unit consisting of a word or a group of words that is separate from any other grammatical construction) it can also include other portions of the subject text such as a chapters, sections, paragraphs, phrases, words, lines, word or phrase types, part of speech, morphemes, syllables, punctuation marks, numbers, characters, symbols or any other desired division of the subject text. 
       FIG. 2  further shows that the method  200  can include setting  204  a repeat factor to zero. In at least one implementation, the repeat factor can indicate the number of instances in which one or more repeated sub-units have been encountered in the unit. In particular, the repeat factor can allow the correct occurrence of the repeated sub-unit to be identified, as described below. 
       FIG. 2  also shows that the method  200  can include identifying  206  sub-units in the unit. In at least one implementation, the sub-unit can include any sub-unit of the unit which is to be analyzed in the word graph. I.e., a sub-unit can include a unique entry to be analyzed within the word graph. For example, a sub-unit can include chapters, sections, paragraphs, sentences, phrases, words, lines, word or phrase types, part of speech, morphemes, syllables, punctuation marks, numbers, characters, symbols or any other desired division of the subject text. 
       FIG. 2  additionally shows that the method  200  can include determining  208  whether any sub-units remain to be processed. In at least one implementation, determining  206  whether any sub-units remain to be processed can include determining when the end of the unit has been reached. For example, the unit may include only a single sub-unit or multiple sub-units. Each sub-unit can be analyzed in turn, as described below. 
       FIG. 2  also shows that the method can include setting  210  a current cursor to the current sub-unit. In at least one implementation, the current cursor can indicate the sub-unit to be processed. For example, the current cursor can include a pointer which identifies the sub-unit being processed. 
       FIG. 2  further shows that the method  200  can include determining  212  whether a prior cursor has been set. In at least one implementation, the prior cursor can identify the sub-unit immediately preceding the current sub-unit. In particular, the prior cursor can point to the sub-unit prior to the current sub-unit in the unit being analyzed. If the prior cursor has not been set, that is an indication that the sub-unit being analyzed is the first sub-unit in the unit. In contrast, if the prior cursor has been set, that is an indication that the sub-unit being analyzed is not the first sub-unit in the unit. This distinction can be captured, as described below. 
       FIG. 2  additionally shows that the method  200  can include creating  214  an entry in the index of first sub-units if the prior cursor is not set. In at least one implementation, the entry can link the unit identifier to the first sub-unit. I.e., the unit identifier can point to the first sub-unit in the unit. The unit identifier can be changed to include the value of the current cursor, which is pointing to the first sub-unit, or can include any other method of indicating the first sub-unit of the unit. 
       FIG. 2  also shows that the method  200  can include processing  216  the identified sub-unit pair if the prior cursor is set. In at least one implementation, the sub-unit pair is the current sub-unit and the prior sub-unit. I.e., the sub-unit pair to be processed  216  are the sub-units identified by the prior cursor and the current cursor. 
       FIG. 2  further shows that the method  200  can include setting  218  the prior cursor to the current cursor. In at least one implementation, setting  218  the prior cursor to the current cursor can include copying the value of the current cursor to the prior cursor. In particular, the prior cursor and the current cursor will be indicating the same entry in preparation for the processing of the next sub-unit pair. I.e., the next sub-unit pair can include the current sub-unit and the sub-unit following the current sub-unit. 
       FIG. 3  illustrates an example of a method  300  for processing a sub-unit pair of a subject text. In at least one implementation, the sub-units will not only be individually stored, but their relationship to one another, and therefore their relationship to other sub-units, will also be mapped. For example, each sub-unit can point to the immediately subsequent sub-unit, as described below. 
       FIG. 3  shows that the method  300  can include attempting  302  to locate a node representing the first sub-unit in the sub-unit pair. In at least one implementation, the presence of a node indicates that the first sub-unit in the sub-unit pair has been previously encountered and the absence of a node indicates that the first sub-unit in the sub-unit pair has not been previously encountered. The node can be located using any desired search method. For example, the word graph can include an index of sub-units already stored. Additionally or alternatively, the word graph can be crawled, in an attempt to locate the first sub-unit. I.e., any desired method for searching the word graph is contemplated herein. 
       FIG. 3  also shows that the method  300  can include proceeding  304  with the existing node if the node is located  302 . In at least one implementation, the same node can store the information for each use of the first sub-unit. I.e., the node can be reused for every instance of the first sub-unit (both within the current unit and within one or more other units). In each instance, the first sub-unit can be linked to the second sub-unit in the sub-unit pair, as described below. 
       FIG. 3  further shows that the method  300  can include adding  306  a node if the node is not located  302 . In at least one implementation, adding  306  the node can include storing the first sub-unit in memory. Additionally or alternatively, adding  306  the node can include pointing to the first sub-unit in memory or otherwise indicating the existence of the first sub-unit. 
       FIG. 3  additionally shows that the method  300  can include attempting  308  to locate a node representing the second sub-unit in the sub-unit pair. In at least one implementation, the presence of a node indicates that the second sub-unit in the sub-unit pair has been previously encountered and the absence of a node indicates that the second sub-unit in the sub-unit pair has not been previously encountered. The node can be located using any desired search method. For example, the word graph can include an index of sub-units already stored. Additionally or alternatively, the word graph can be crawled, in an attempt to locate the second sub-unit. I.e., any desired method for searching the word graph is contemplated herein. 
       FIG. 3  also shows that the method  300  can include proceeding  310  with the existing node if the node is located  308 . In at least one implementation, the same node can store the information for each use of the second sub-unit. I.e., the node can be reused for every instance of the second sub-unit (both within the current unit and within one or more other units). In each instance, the second sub-unit can be linked to the first sub-unit in the sub-unit pair, if desired. 
       FIG. 3  further shows that the method  300  can include adding  312  a node if the node is not located  308 . In at least one implementation, adding  312  the node can include storing the second sub-unit in memory. Additionally or alternatively, adding  312  the node can include pointing to the second sub-unit in memory or otherwise indicating the existence of the second sub-unit. 
       FIG. 3  additionally shows that the method  300  can include determining  314  if the second sub-unit has previously occurred for this unit. In at least one implementation, each node can include a unit identifier which indicates which units contain the sub-units. I.e., each node can identify in which units the sub-unit has been located. Therefore, determining  314  if the second sub-unit has previously occurred for this unit can include checking the second node to determine if the current unit identifier is present in the second node. 
       FIG. 3  also shows that the method  300  can include incrementing  316  the repeat factor if the second sub-unit has previously occurred in this unit. In at least one implementation, the repeat factor indicates instances in which one or more repeated sub-units have been encountered in the unit. I.e., the repeat factor can indicate the total number of sub-unit repeats, although the sub-unit repeats may be different than one another. I.e., one or both of the sub-units near the second sub-unit can be different in the current occurrence than in previous occurrences. 
       FIG. 3  further shows that the method  300  can include proceeding  318  with the current repeat factor if the second sub-unit has not occurred in the current unit. In at least one implementation, the lack of a previous occurrence of the second sub-unit indicates that the second sub-unit is not being repeated within the unit. Therefore, the second sub-unit is not a repeat and the repeat factor does not need to be incremented. 
       FIG. 3  additionally shows that the method  300  can include creating  320  a link that indicates the current sub-unit pair. In at least one implementation, the link can be unique for each occurrence of the sub-unit pair. I.e., even if the sub-unit pair has previously been encountered in the unit, subsequent sub-units may be different and the link can reflect the relative occurrence of the sub-unit pair. Additionally or alternatively, the link can indicate the current repeat factor within the unit. I.e., the repeat factor can be saved within the link, which includes the relative occurrence of the second sub-unit. 
       FIGS. 4A ,  4 B and  4 C illustrate an example of adding a sentence to a word graph  400 .  FIG. 4A  illustrates an example of adding the first word of a sentence to a word graph  400 ;  FIG. 4B  illustrates an example of adding the second word of the sentence to the word graph  400 ; and  FIG. 4C  illustrates an example of a complete sentence in the word graph  400 . In at least one implementation, the word graph  400  can map each word in the sentence and the links between the words. The example shown in  FIGS. 4A ,  4 B and  4 C depicts the addition of the words in the sentence “Dick and Jane go up a hill” to the word graph  400 ; however, one of skill in the art will appreciate that the word graph  400  can be used for any sub-units in a unit, as defined above. I.e., one of skill in the art will appreciate that the use of words as sub-units and sentences as units in  FIGS. 4A ,  4 B and  4 C is exemplary and is not limiting herein. 
       FIG. 4A  shows that the first word  402  is added to the word graph  400 . In at least one implementation, the first word  402  is placed in a node, which becomes part of the word graph  400 . The first word  402  can be linked to an index of first words. In particular, an index can be kept which lists the first word or each of the first words in the word graph  400 . This can allow the evaluation of each sentence individually or of each of the sentences, or any portion thereof, to proceed through each sentence if so desired. 
       FIG. 4B  shows that the first word  402  is given a sentence identifier  404 . In at least one implementation, the sentence identifier  404  can indicate the sentence being evaluated. The sentence identifier  404  can include any identifier which provides the desired information about the sentence. For example, the sentence identifier  404  can include the number of the sentence in numerical order. Additionally or alternatively, the sentence identifier  404  can include information about the subject text (including authorship and/or other source information), the location of the sentence, the date and/or time the sentence was added to the word graph  400  or any other desired information. 
       FIG. 4B  also shows that the second word  406  is added to the word graph  400 . The second word can include the word immediately following the first word. In at least one implementation, the second word  406  is added to a second node which becomes part of the word graph  400 . 
       FIG. 4B  further shows that the word graph  400  can include a link  408  from the first word  402  to the second word  406 . In at least one implementation, the link  408  shows that the second word  406  follows the first word  402  in the sentence. In different sentences the word order can be different or the first word  402  or the second word  406  can be different, so the link  408  shows the relationship between the two words in the sentence for analysis. 
       FIG. 4B  additionally shows that the word graph  400  can include the repeat factor  410  for the current sentence. In at least one implementation, the repeat factor  410  can show the number of instances in which one or more repeated words have been encountered in the sentence. I.e., the repeat factor allows the word graph  400  to show which instance is occurring of a word which may be repeated in the sentence, as described above. 
       FIG. 4C  shows that the word graph can include subsequent words  412 . In at least one implementation, each subsequent word  412  can include a link  408  which shows the next word in the sentence. I.e., each subsequent word  412  is pointed to by the preceding word and points to the next word. Additionally or alternatively, each subsequent word  412  can include the repeat factor  410 . 
       FIG. 4C  also shows that the word graph  400  can include a final word  414 . In at least one implementation, the final word  414  can lack a pointer. I.e., the lack of a link to another word can indicate that the final word  414  is the last word in the sentence. Additionally or alternatively, the final word  414  can include a tag or other indicator that it is the last word. The repeat factor  410  can be omitted from the last word  414  since there is no danger of following the wrong trail without a pointer. I.e., the lack of a link  408  can render the use of the repeat factor  410  moot. 
       FIGS. 5A and 5B  illustrate an example of adding a second sentence to a word graph  400 .  FIG. 5A  illustrates an example of adding the second sentence to the word graph  400  while in process; and  FIG. 5B  illustrates an example of adding a complete second sentence to the word graph  400 . In at least one implementation, adding the second sentence can use nodes already existing in the word graph  400  if available. I.e., once a node has been created in any sentence, it can be used to show the presence of the word in other sentences (or later within the same sentence), as described below. The example shown in  FIGS. 5A and 5B  depicts the addition of the sentence “Jane wants a hill to go up” to the word graph  400 ; however, one of skill in the art will appreciate that the word graph  400  can be used for any sub-units in a unit, as defined above. I.e., one of skill in the art will appreciate that the use of words as sub-units and sentences as units in  FIGS. 5A and 5B  is exemplary and is not limiting herein. 
       FIG. 5A  shows that the word graph  400  can include a first word  502  of the second sentence. In at least one implementation, the first word  502  can be placed in a node, which becomes part of the word graph  400 . If the first word  502  is a word that occurred in an earlier sentence, then the node from the earlier sentence can be used. Additionally or alternatively, if the first word  502  did not occur in an earlier sentence, then a new node can be created for the first word  502 . In the example shown, the first word  502  occurred in the first sentence so the node can be reused. If an index is kept which lists the first word or each of the first words in the word graph  400  the first word  502  can be added to the index. 
       FIG. 5A  also shows that the first word  502  is given a sentence identifier  504 . In at least one implementation, the sentence identifier  504  can indicate the sentence being evaluated. In particular, the sentence identifier  504  can be different than the sentence identifier  404  which identifies the first sentence. The sentence identifier  504  can include any identifier which provides the desired information about the sentence. For example, the sentence identifier  504  can include the sentence in numerical order. Additionally or alternatively, the sentence identifier  504  can include information about the subject text, the location of the sentence, the date and/or time the sentence was added to the word graph  400  or any other desired information (including authorship and/or other source information). 
       FIG. 5A  further shows that a second word  506  can be added to the word graph  400 . In at least one implementation, the second word  506  can be placed in a node, which becomes part of the word graph  400 . If the second word  506  is a word that occurred in an earlier sentence (or earlier in the same sentence), then the node from the earlier sentence (or earlier in the same sentence) can be used. Additionally or alternatively, if the second word  506  did not occur in an earlier sentence (or earlier in the same sentence), then a new node can be created for the second word  506 . 
       FIG. 5A  additionally shows that the word graph  400  can include a link  508  from the first word  502  to the second word  506 . In at least one implementation, the link  508  shows that the second word  506  follows the first word  502  in the second sentence. In different sentences the word order can be different or the first word  502  or the second word  506  can be different, so the link  508  shows the relationship between the two words in the second sentence for analysis. The link  508  can be associated with the second sentence identifier  506  to distinguish the use of the first word  502  in the second sentence from the use of the first word  502  in the first sentence. 
       FIG. 5A  additionally shows that the word graph  400  can include the repeat factor  510  for the second sentence. In at least one implementation, the repeat factor  510  can show the number of instances in which one or more repeated words have been encountered in the second sentence. I.e., the repeat factor allows the word graph  400  to show which instance is occurring of a word which may be repeated in the second sentence, as described above. 
       FIG. 5B  shows that the word graph can include subsequent words  512 . In at least one implementation, each subsequent word  512  can include a link which shows the next word in the sentence. I.e., each subsequent word  512  is pointed to by the preceding word and points to the next word. The subsequent words  512  can be placed in new nodes or existing nodes as needed. 
       FIG. 5B  also shows that the word graph  400  can include a final word  514 . In at least one implementation, the final word  514  can lack a pointer. I.e., the lack of a link  508  to another word can indicate that the final word  514  is the last word in the sentence. Additionally or alternatively, the final word  514  can include a tag or other indicator that it is the last word. The repeat factor  510  can be omitted from the last word  514  since there is no danger of following the wrong trail without a pointer. I.e., the lack of a link  508  can render the use of the repeat factor  510  moot. 
       FIG. 6  illustrates an example of adding a complete third sentence to a word graph  400 . In at least one implementation, adding the third sentence can use nodes already existing in the word graph  400  if available. I.e., once a node has been created in any sentence, it can be used to show the presence of the word in other sentences (or later within the same sentence), as described below. The example shown in  FIG. 6  shows the addition of the sentence “To go up and up and up is fun” to the word graph  400 ; however, one of skill in the art will appreciate that the word graph  400  can be used for any sub-units in a unit, as defined above. I.e., one of skill in the art will appreciate that the use of words as sub-units and sentences as units in  FIG. 6  is exemplary and is not limiting herein. 
       FIG. 6  shows that the repeat factor  602  of the third sentence is incremented when a word is repeated. In at least one implementation the repeat factor  602  is incremented each time a repeated word is encountered. This incremented repeat factor is applied to subsequent words in the sentence, whether or not they are themselves repeated. For example, the word “up” occurs three times in the sentence. After the first instance of “and” is added to the word graph  400  the word “up” occurs for the second time. Therefore, the second instance of the word “up” causes the repeat factor  602  to become 1 because the repeat factor  602  has been incremented. In addition, after the second instance of “up” is added to the word graph  400 , the word “and” occurs for the second time. Therefore, the second instance of the word “and” causes the repeat factor  602  to become 2 because the repeat factor  602  has been incremented. This continues throughout the sentence. 
       FIG. 7 , and the following discussion, are intended to provide a brief, general description of a suitable computing environment in which the invention may be implemented. Although not required, the invention will be described in the general context of computer-executable instructions, such as program modules, being executed by computers in network environments. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps. 
     One skilled in the art will appreciate that the invention may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, mobile phones, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. The invention may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination of hardwired or wireless links) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. 
     With reference to  FIG. 7 , an example system for implementing the invention includes a general purpose computing device in the form of a conventional computer  720 , including a processing unit  721 , a system memory  722 , and a system bus  723  that couples various system components including the system memory  722  to the processing unit  721 . It should be noted, however, that as mobile phones become more sophisticated, mobile phones are beginning to incorporate many of the components illustrated for conventional computer  720 . Accordingly, with relatively minor adjustments, mostly with respect to input/output devices, the description of conventional computer  720  applies equally to mobile phones. The system bus  723  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The system memory includes read only memory (ROM)  724  and random access memory (RAM)  725 . A basic input/output system (BIOS)  726 , containing the basic routines that help transfer information between elements within the computer  720 , such as during start-up, may be stored in ROM  724 . 
     The computer  720  may also include a magnetic hard disk drive  727  for reading from and writing to a magnetic hard disk  739 , a magnetic disk drive  728  for reading from or writing to a removable magnetic disk  729 , and an optical disc drive  730  for reading from or writing to a removable optical disc  731  such as a CD-ROM or other optical media. The magnetic hard disk drive  727 , magnetic disk drive  728 , and optical disc drive  730  are connected to the system bus  723  by a hard disk drive interface  732 , a magnetic disk drive-interface  733 , and an optical drive interface  734 , respectively. The drives and their associated computer-readable media provide nonvolatile storage of computer-executable instructions, data structures, program modules and other data for the computer  720 . Although the exemplary environment described herein employs a magnetic hard disk  739 , a removable magnetic disk  729  and a removable optical disc  731 , other types of computer readable media for storing data can be used, including magnetic cassettes, flash memory cards, digital versatile discs, Bernoulli cartridges, RAMs, ROMs, and the like. 
     Program code means comprising one or more program modules may be stored on the hard disk  739 , magnetic disk  729 , optical disc  731 , ROM  724  or RAM  725 , including an operating system  735 , one or more application programs  736 , other program modules  737 , and program data  738 . A user may enter commands and information into the computer  720  through keyboard  740 , pointing device  742 , or other input devices (not shown), such as a microphone, joy stick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  721  through a serial port interface  746  coupled to system bus  723 . Alternatively, the input devices may be connected by other interfaces, such as a parallel port, a game port or a universal serial bus (USB). A monitor  747  or another display device is also connected to system bus  723  via an interface, such as video adapter  748 . In addition to the monitor, personal computers typically include other peripheral output devices (not shown), such as speakers and printers. 
     The computer  720  may operate in a networked environment using logical connections to one or more remote computers, such as remote computers  749   a  and  749   b . Remote computers  749   a  and  749   b  may each be another personal computer, a server, a router, a network PC, a peer device or other common network node, and typically include many or all of the elements described above relative to the computer  720 , although only memory storage devices  750   a  and  750   b  and their associated application programs  736   a  and  736   b  have been illustrated in  FIG. 7 . The logical connections depicted in  FIG. 7  include a local area network (LAN)  751  and a wide area network (WAN)  752  that are presented here by way of example and not limitation. Such networking environments are commonplace in office-wide or enterprise-wide computer networks, intranets and the Internet. 
     When used in a LAN networking environment, the computer  720  can be connected to the local network  751  through a network interface or adapter  753 . When used in a WAN networking environment, the computer  720  may include a modem  754 , a wireless link, or other means for establishing communications over the wide area network  752 , such as the Internet. The modem  754 , which may be internal or external, is connected to the system bus  723  via the serial port interface  746 . In a networked environment, program modules depicted relative to the computer  720 , or portions thereof, may be stored in the remote memory storage device. It will be appreciated that the network connections shown are exemplary and other means of establishing communications over wide area network  752  may be used. 
     The present invention may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.