JP2012093966A - Document analysis apparatus and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a document analysis apparatus and program capable of extracting a feature word appropriate for mutually comparing contents of a plurality of categories.SOLUTION: Word extraction means extracts a word contained in a plurality of documents belonging to a first category indicated by category information stored in category information storage means. Document number calculation means calculates a first document number indicating the number of documents in which the extracted word appears in the plurality of documents belonging to the first category, and a second document number indicating the number of documents in which the extracted word appears in a plurality of documents belonging to a second category positioned under the first category. Feature degree calculation means calculates a feature degree of the extracted word on the basis of the number of documents belonging to the first category, the number of document belonging to the second category, the number of the second categories, and the first and second document numbers. Feature word extraction means extracts a feature word for the first category on the basis of the feature degree.

Description

  Embodiments described herein relate generally to a document analysis apparatus and program for analyzing a large number of digitized documents.

  In recent years, with the increase in performance of computers, the increase in capacity of storage media, the spread of computer networks, etc., a large amount of electronic documents can be stored and managed in a computer system and used for various purposes. . Here, for example, documents include business documents such as forms, planning documents, design documents or minutes, technical documents such as academic papers, product manuals, patent documents, news articles, e-mails, web pages, etc. Documents shared on such networks.

  However, simply storing such a large number of documents in an unorganized manner, for example, in a file system or database, makes it impossible to know where and what information (documents) exists, and the large amount of documents stored and managed in the computer system. The document cannot be used effectively.

  Therefore, in order to promote effective use and sharing of information, it is preferable to classify (organize) a large number of documents according to the content or use.

  In addition, by analyzing or investigating a large number of documents classified in this way, a technology for grasping the tendency of the contents of the document or obtaining new knowledge has been developed.

  In addition, as described above, a set of documents classified as a result of the classification of a large number of documents, for example, attributes included in each document (if the document is a patent document, for example, “applicant” or “application date” A document set that is classified using information such as “)” or a document set that is generated by document clustering that classifies documents according to the similarity of the contents of each document is called a category.

  By the way, when a large number of documents are classified as described above, there is a need to know the overall image and the characteristics of each category (document set belonging to). Specifically, for example, in the field of patent search, when searching for technical trends (application trends) by applicant (company), there is a need to know the characteristics of each applicant's category. . In addition, when it is desired to grasp the change in technology for each application year, there is a need to know the characteristics of the category of each application year. Further, in a newspaper article or the like, when it is desired to know a transition of a topic related to a certain case, there is a need to know characteristics of a topic category related to the case.

  In order to meet such needs, for example, a technique (hereinafter referred to as a feature word extraction technique) for extracting a characteristic word (hereinafter referred to as a feature word) in a document set (category) to be analyzed is known. ing. According to this feature word extraction technology, by extracting feature words of a document set classified into a category and presenting the feature words, the user can select the feature of the category (the contents of the document set classified into the category). ) Can be easily understood.

JP 2005-128978 A JP 2006-302107 A JP 2005-326897 A

  However, the category feature words extracted by the feature word extraction technique described above are insufficient to compare different categories.

  The feature words extracted by the above feature word extraction technique are extracted from the category or the entire document set classified into the category. For this reason, when comparing feature categories using different category feature words, the user must determine which feature words extracted from each category should be compared with each other. Is difficult. In particular, if the number of categories to be compared and the number of feature words extracted from each of the categories increase, the difficulty level further increases. In other words, when feature words are simply extracted for each category, for example, by presenting the number of documents including the feature words in each category to the user, the user can grasp the features of each category. It is difficult to grasp characteristics between a plurality of categories such as common points or differences between the categories.

  In addition, the feature words extracted from the categories (the entire document set classified into the categories) do not take into account the trends of other categories (for example, the appearance tendency of words). For this reason, even if cross-tabulation, for example, is performed using feature words extracted in this way, the map may become a uniform map as a whole or an extremely sparse map. It is not suitable for comparing. In other words, the feature words extracted by the above-described feature word extraction technique may include words that are not suitable for comparison between categories.

  Therefore, the problem to be solved by the present invention is to provide a document analysis apparatus and program capable of extracting feature words suitable for comparing the contents of a plurality of categories with each other.

  The document analysis apparatus according to the embodiment includes a document storage unit, a category storage unit, a word extraction unit, a document number calculation unit, a feature degree calculation unit, and a feature word extraction unit.

  The document storage means stores a plurality of documents including words belonging to a plurality of categories constituting a hierarchical structure.

  The category storage means stores category information indicating documents belonging to the category for each category constituting the hierarchical structure.

  The word extraction means stores words included in a plurality of documents belonging to the first category designated by the user indicated by the category information stored in the category information storage means. Extract from multiple documents.

  The document number calculation means is a first document number indicating the number of documents in which the extracted word appears in a plurality of documents belonging to the first category based on the category information stored in the category storage means. In addition, a second document number indicating the number of documents in which the extracted word appears in a plurality of documents belonging to a second category positioned below the first category in the hierarchical structure is calculated.

  The feature degree calculating means is based on the number of documents belonging to the first category, the number of documents belonging to the second category, the number of second categories, and the calculated first and second document numbers. Then, the characteristic degree of the extracted word with respect to the first category is calculated.

  The feature word extraction unit extracts the extracted word as a feature word for the first category based on the feature degree calculated by the feature degree calculation unit.

1 is a block diagram showing a hardware configuration of a document analysis apparatus according to a first embodiment. The block diagram which mainly shows a function structure of the document analyzer 30 shown in FIG. The figure which shows an example of the data structure of the document stored in the document storage part 22 shown in FIG. The figure which shows an example of the document described in the XML format. The figure which shows an example of the data structure of the category information stored in the category storage part 23 shown in FIG. The figure which shows an example of the data structure of the category information stored in the category storage part 23 shown in FIG. The figure which shows an example of the data structure of the category information stored in the category storage part 23 shown in FIG. The figure which shows an example of the data structure of the category information stored in the category storage part 23 shown in FIG. The figure which shows an example of the data structure of the category information stored in the category storage part 23 shown in FIG. 6 is a flowchart showing a processing procedure of the document analysis apparatus 30 according to the present embodiment. The figure which shows an example of the category hierarchical structure presentation screen when the hierarchical structure of a category is shown by the category presentation part 311. FIG. The figure which shows an example of a feature word extraction request | requirement screen. The flowchart which shows the process sequence of the word extraction process performed by the word extraction part 321. FIG. The figure which shows an example of the data structure of the word data storage part after a word extraction process is performed. The flowchart which shows the process sequence of the feature word extraction process performed by the feature word extraction part 322. FIG. The figure which shows an example of the data structure of the word data storage part after the characteristic degree with respect to the category set CS calculated for every word in word data is stored. The block diagram which mainly shows the function structure of the document analyzer which concerns on 2nd Embodiment. 6 is a flowchart showing a processing procedure of the document analysis apparatus 50 according to the present embodiment. The figure which shows an example of the category hierarchical structure presentation screen when the hierarchical structure of a category is shown by the category presentation part 311. FIG. The figure which shows an example of a cross tabulation request | requirement screen. 10 is a flowchart showing a processing procedure of feature word characteristic calculation processing executed by a feature word characteristic calculation unit 521; The figure which shows an example of the data structure of the word data storage part after the common degree with respect to the category set CS calculated for every feature word is stored. The flowchart which shows the process sequence of the feature word category production | generation process performed by the feature word category production | generation part 522. The figure which shows an example of the data structure of the category information regarding the intermediate category Cp. The figure which shows an example of the data structure of the category information regarding the feature word category produced | generated using the feature word "display". The figure which shows an example of the data structure of the category information regarding the feature word category produced | generated using the feature word "operation". The flowchart which shows the process sequence of the cross tabulation process performed by the cross tabulation part 511. FIG. The figure which shows an example of the cross tabulation result shown to the user by the user interface part 51. FIG.

  Hereinafter, each embodiment will be described with reference to the drawings.

(First embodiment)
First, the first embodiment will be described with reference to FIGS. 1 and 2. FIG. 1 is a block diagram showing a hardware configuration of the document analysis apparatus according to the present embodiment. As shown in FIG. 1, the computer 10 is connected to an external storage device 20 such as a hard disk drive (HDD). The external storage device 20 stores a program 21 executed by the computer 10. The computer 10 and the external storage device 20 constitute a document analysis device 30.

  FIG. 2 is a block diagram mainly showing a functional configuration of the document analysis apparatus 30 shown in FIG. As shown in FIG. 2, the document analysis device 30 includes a user interface unit 31 and an analysis axis generation unit 32. In the present embodiment, these units 31 and 32 are realized by the computer 10 illustrated in FIG. 1 executing the program 21 stored in the external storage device 20. This program 21 can be stored in advance in a computer-readable storage medium and distributed. Further, this program 21 may be downloaded to the computer 10 via, for example, a network.

  Further, the document analysis device 30 includes a document storage unit 22 and a category storage unit 23. In the present embodiment, the document storage unit 22 and the category storage unit 23 are stored in, for example, the external storage device 20.

  The document storage unit 22 stores a plurality of documents (data) to be analyzed by the document analysis device 30. Each of the documents stored in the document storage unit 22 includes, for example, a word (character string) and belongs (categorized) to a category constituting a hierarchical structure.

  The category storage unit 23 stores information related to the category (category information) for each category constituting the hierarchical structure to which the document belongs. The category information related to the category includes, for example, information indicating a category (upper category) positioned above the category in the hierarchical structure, a document belonging to the category, a category name of the category, and the like.

  The document storage unit 22 and the category storage unit 23 are realized by a file system, a document database, or the like, but may be configured by a plurality of storage units (not shown) connected by a computer network, for example.

  The user interface unit 31 includes a category presentation unit 311. The category presenting unit 311 presents the hierarchical structure of categories to the user using, for example, category information stored in the category storage unit 23. Note that the user can specify a category to be analyzed (analysis axis) from the categories presented by the category presentation unit 311.

  The analysis axis generation unit 32 has a function of extracting feature words for comparing the contents of categories (second categories) positioned below the category (first category) designated by the user in the hierarchical structure. Have. The analysis axis generation unit 32 includes a word extraction unit 321 and a feature word extraction unit 322. In the following description, the category designated by the user is referred to as an analysis axis category Ctgt.

  The word extraction unit 321 refers to the category information stored in the category storage unit 23 and performs, for example, morphological analysis on a plurality of documents belonging to the analysis axis category Ctgt stored in the document storage unit 22. Thereby, the word extraction unit 321 extracts words included in a plurality of documents belonging to the analysis axis category Ctgt from the plurality of documents.

  The word extraction unit 321 calculates the number of documents in which the extracted word appears in a plurality of documents belonging to the analysis axis category Ctgt (first document number) with reference to the document storage unit 22 and the category storage unit 23. .

  Also, the word extraction unit 321 determines the number of documents (second document number) that appear in a plurality of documents in which the extracted words belong to a category positioned below the analysis axis category Ctgt in the hierarchical structure. And the category storage unit 23 is referred to.

  The feature word extraction unit 322 includes the number of documents that belong to the analysis axis category Ctgt, the number of documents that belong to a category that is positioned below the analysis axis category Ctgt in the hierarchical structure, the number of the categories, and the document calculated by the word extraction unit 321. Based on the number (first and second document numbers), the feature degree of the word extracted by the word extraction unit 321 for the analysis axis category Ctgt is calculated.

  The feature word extraction unit 322 extracts the word extracted by the word extraction unit 321 as a feature word for the analysis axis category Ctgt based on the calculated word feature.

  Note that the feature words extracted by the feature word extraction unit 322 are presented to the user via the user interface unit 31, for example.

  FIG. 3 shows an example of the data structure of a document stored in the document storage unit 22 shown in FIG. The document 221 shown in FIG. 2 is assumed to be a document (patent document) in which information related to a patent is described.

  As shown in FIG. 3, the document 221 includes a document number that is an identifier for uniquely identifying the document 221. Further, the document 221 includes text related to the document 221, that is, a document name and a body described in, for example, a natural language. Further, the document 221 includes attribute information according to the purpose or format of the document 221. The document 221 shown in FIG. 3 includes, as attribute information, the filing date related to the patent application and the applicant who filed the patent application.

  As shown in FIG. 4, the document 221 stored in the document storage unit 22 can be described and managed in, for example, an XML (eXtensible Markup Language) format. In this case, text (document name and body text) and attribute information (application date and applicant) included in the document 221 are described using XML elements (tags) and the like.

  5 to 9 show examples of the data structure of category information stored in the category storage unit 23 shown in FIG. The category information is stored in the category storage unit 23 for each category constituting the hierarchical structure.

  As shown in FIGS. 5 to 9, the category information includes a category number, a higher category number, a category name, and a classification rule.

  The category number is an identifier for uniquely identifying the category. The upper category number indicates a category number for identifying a category (upper category) located in the upper level of the category identified by the category number in the hierarchical structure. The category name indicates the name of the category identified by the category number. The classification rule indicates documents belonging to a category identified by a category number.

  FIG. 5 shows an example of the data structure of the category information regarding the category of the root (hereinafter referred to as the root category) in the category hierarchical structure.

  In the example illustrated in FIG. 5, the category information 231 includes a category number “C000”, a category name “route”, and a classification rule “true (constant)”. This indicates that the category name of the root category identified by the category number “C000” is “root”. Here, the classification rule “true (constant)” represents that all documents satisfy the condition. That is, it is shown that all the documents stored in the document storage unit 22 belong to the category (root category) identified by the category number “C000” included in the category information 231. Note that the category identified by the category number “C000” is the root category, and there is no higher category of the category in the hierarchical structure. Therefore, the higher category number included in the category information 231 is “(none)”. .

  FIG. 6 shows an example of a data structure of category information related to a category located under the root category in the category hierarchical structure.

  In the example shown in FIG. 6, the category information 232 includes a category number “C001”, a higher category number “C000”, a category name “by applicant”, and a classification rule “true”. This indicates that the upper category of the category identified by the category number “C001” is the category identified by the upper category number “C000” (that is, the root category). Further, the category name of the category identified by the category number “C001” is “By Applicant”, and it is indicated that all the documents stored in the document storage unit 22 belong to the category.

  FIG. 7 shows an example of a data structure of category information related to a category located under the root category in the category hierarchical structure.

  In the example illustrated in FIG. 7, the category information 233 includes a category number “C002”, a higher category number “C000”, a category name “by application year”, and a classification rule “true”. This indicates that the upper category of the category identified by the category number “C002” is the category identified by the upper category number “C000” (that is, the root category). Further, the category name of the category identified by the category number “C002” is “by application year”, which indicates that all documents stored in the document storage unit 22 belong to the category.

  FIG. 8 shows an example of the data structure of the category information related to the category located under the category identified by the category number “C001” included in the category information 232 shown in FIG. 6 in the category hierarchical structure.

  In the example illustrated in FIG. 8, the category information 234 includes a category number “C101”, a higher category number “C001”, a category name “Company A”, and a classification rule “./Applicant=“Company A” ”. Yes. This indicates that the upper category of the category identified by the category number “C101” is the category identified by the upper category number “C001”. Further, it is indicated that the category name of the category identified by the category number “C101” is “Company A”.

  Here, the classification rule “./applicant=“Company A” ”included in the category information 234 is a rule on condition that the applicant, which is attribute information included in the document, is the company A. That is, according to the classification rule “./applicant=“Company A” ”, the category identified by the category number“ C101 ”includes a document in which the applicant is Company A (that is, the applicant“ Documents including “Company A” belong.

  The classification rule “./applicant=“Company A” ”is an example in which conditions for a document described in the XML format described above are described. Here, it is described using a query language called XQuery (or XPath).

  FIG. 9 shows an example of the data structure of the category information related to the category located in the lower level of the category identified by the category number “C002” included in the category information 233 shown in FIG. 7 in the category hierarchical structure.

  In the example shown in FIG. 9, the category information 235 includes a category number “C206”, a higher category number “C002”, a category name “2008”, and a classification rule “./application date> =” 2008/01/01 ”and ./Application date <= ”2008/12/31” ”is included. This indicates that the upper category of the category identified by the category number “C206” is the category identified by the upper category number “C002”. Further, it is indicated that the category name of the category identified by the category number “C206” is “2008”.

  Here, the classification rules “./application date> =” 2008/01/01 ”and ./application date <=” 2008/12/31 ”included in the category information 235 are attribute information included in the document. Is a rule on condition that the filing date is 2008 (that is, from January 1, 2008 to December 31, 2008). That is, according to this classification rule “./application date> =” 2008/01/01 ”and ./application date <=” 2008/12/31 ”, the category identified by the category number“ C206 ” , A document whose application date is 2008 (that is, a document including application dates “2008/01/01” to “2008/12/31” as attribute information) belongs. The classification rules “./application date> =” 2008/01/01 ”and ./application date <=“ 2008/12/31 ”” are described using the above-described query language called XQuery. .

  Here, for example, when the category information 235 shown in FIG. 7 is category information related to a category located under the category identified by the category number “C101”, that is, the upper category number included in the category information 235 is “C101”. Assuming that In this case, the category identified by the category number “C206” included in the category information 235 includes a document whose applicant is company A and whose application date is 2008. That is, a document that matches the AND condition of the classification rule included in the category information related to the category and the classification rule included in the category information related to the upper category of the category belongs to the category at a lower position in the hierarchical structure. In other words, the classification rule included in the category information acts as an AND condition for the hierarchical structure of the category. According to this, a document that belongs to a category that is higher in the category belongs to a category that is lower in the hierarchical structure.

  The classification rules included in the category information 234 and 235 shown in FIGS. 8 and 9 have been described as being described using a query language called XQuery. However, the description format of the classification rules is the document described above. What is necessary is just to determine according to the realization method of the storage part 22, or the data format of the document stored in the said document storage part 22, for example, SQL etc. may be used.

  Next, a processing procedure of the document analysis apparatus 30 according to the present embodiment will be described with reference to the flowchart of FIG.

  First, the category presentation unit 311 included in the user interface unit 31 acquires category information stored in the category storage unit 23 (step S1).

  Next, the category presenting unit 311 presents (displays) the category hierarchical structure to the user based on the category number, the upper category number, and the category name included in the acquired category information (step S2).

  Here, FIG. 11 shows an example of a screen when a category hierarchy structure is presented by the category presentation unit 311 (hereinafter referred to as a category hierarchy structure presentation screen).

  As shown in FIG. 11, the category hierarchy structure presentation screen 100 displays the category names of the categories constituting the hierarchical structure in a tree form (category tree). Hereinafter, for example, a category having a category name by applicant is simply referred to as a category “by applicant”. The same applies to other categories.

  In the category hierarchical structure presentation screen 100 shown in FIG. 11, it is shown that the category “by applicant” is a category located under the root category. In the category hierarchical structure presentation screen 100, the categories “Company A”, “Company B”, “Company C”,... It is shown.

  Similarly, the category hierarchical structure presentation screen 100 indicates that the category “by application year” is a category located under the root category. In the category hierarchical structure presentation screen 100, the categories “2004”, “2005”, “2006”, “2007”, and “2008” are categories that are subordinate to the category “by application year”. It is shown that there is.

  In the category hierarchical structure presentation screen 100 shown in FIG. 11, “Root (3000)” is displayed, but the “3000” is stored in the document belonging to the root category (that is, stored in the document storage unit 22). Number of documents).

  When the category hierarchical structure presentation screen 100 is presented by the category presentation unit 311, the user is displayed on the category hierarchical structure presentation screen 100 by referring to the category hierarchical structure presentation screen 100. A category to be analyzed (analysis axis) (hereinafter referred to as an analysis axis category Ctgt) can be designated from a plurality of categories.

  If the category “By Applicant” is designated as the analysis axis category Ctgt on the category hierarchical structure presentation screen 100, for example, a screen (hereinafter referred to as a feature word extraction request screen) 110 shown in FIG. 12 is displayed. .

  As shown in FIG. 12, the feature word extraction request screen 110 displays a sub screen 111 in addition to the hierarchical structure of categories. In this sub-screen 111, for example, feature words are extracted with the category “By Applicant” as the analysis axis. Is displayed.

  The sub-screen 111 is provided with an execution button 111a and a cancel button 111b.

  When the execution button 111a provided on the sub-screen 111 is selected in response to a user operation on the document analysis apparatus 30, feature word extraction for the analysis axis category Ctgt (here, category “by applicant)” is performed. Required for the analyzer 30.

  On the other hand, when a cancel button 111b provided on the sub screen 111 is selected in response to a user operation on the document analysis device 30, a screen for reselecting the analysis axis category Ctgt is displayed as, for example, a category hierarchical structure shown in FIG. A presentation screen 100 is displayed.

  The sub-screen 111 is provided with a column 111c for setting the number of feature words to be extracted, and the user can input a desired numerical value in this column 111c.

  Returning to FIG. 10 again, it is determined whether or not the above feature word extraction is requested in accordance with the user's operation (step S3).

  If it is determined that feature word extraction is requested (YES in step S3), the word extraction unit 321 included in the analysis axis generation unit 32 is a document that belongs to the analysis axis category Ctgt (a set of categories positioned below the analysis axis category Ctgt). The process of extracting the word contained in (3) and calculating the appearance frequency of the extracted word (hereinafter referred to as word extraction process) is executed. Details of the word extraction process will be described later.

  Next, the feature word extraction unit 322 extracts a feature word for the analysis axis category Ctgt (a set of categories positioned below the analysis axis category Ctgt) from the words extracted by the word extraction unit 321 (hereinafter, feature word extraction process). Is written) (step S5). In the feature word extraction process, the feature degree of the word extracted by the word extraction unit 321 is calculated based on the appearance frequency of the word calculated by the word extraction unit 321, and the feature word is calculated based on the feature degree. Extracted. Details of the feature word extraction process will be described later.

  The user interface unit 31 (the category presentation unit 311 included therein) presents the feature word extracted by the feature word extraction unit 322 to the user (step S6).

  As a result, it is possible to present to the user a feature word that is suitable for comparing the contents of categories that are positioned below the analysis axis category Ctgt in the hierarchical structure.

  Next, with reference to the flowchart of FIG. 13, the process procedure of the word extraction process (process of step S4 shown in FIG. 10) performed by the word extraction part 321 mentioned above is demonstrated.

  First, the word extraction unit 321 includes a set of categories (category having the analysis axis category Ctgt as an upper category) positioned below the analysis axis category Ctgt specified by the user in the hierarchical structure (hereinafter referred to as a category set CS). Is specified (step S11). That is, the category set CS includes categories that are positioned below the analysis axis category Ctgt in the hierarchical structure.

  A category included in the category set CS (a category having the analysis axis category Ctgt as a higher category) is a category identified by a category number of category information including a category number for identifying the analysis axis category Ctgt as a higher category number. It is.

  Here, it is assumed that the category set CS specified by the word extraction unit 321 includes a categories of categories C1, C2,.

  The word extraction unit 321 prepares an empty document set Dall used in the following processing (step S12).

  Next, the word extraction unit 321 performs the following processes of steps S13 and S14 for the category Ci (i = 1, 2,..., A) included in the category set CS.

  The word extraction unit 321 acquires category information regarding the category Ci from the category storage unit 23. Here, the word extraction unit 321 acquires category information including a category number for identifying the category Ci.

  Based on the acquired category information, the word extraction unit 321 acquires a set of documents belonging to the category Ci (hereinafter referred to as document set Di) from the document storage unit 22 (step S13). In this case, the word extraction unit 321 acquires a set of documents that match the condition indicated by the classification rule included in the acquired category information as the document set Di.

  The word extraction unit 321 sets the union of the acquired document set Di and document set Dall as the document set Dall in the subsequent processing (step S14).

  Next, the word extraction unit 321 determines whether or not the processing in steps S13 and S14 has been executed for all categories (that is, categories C1, C2,..., Ca) included in the category set CS (step S15). .

  If it is determined that the process has not been executed for all categories (NO in step S15), the process returns to the above-described step S13 and is repeated.

  As described above, by repeating the processes of steps S13 and S14 for all categories included in the category set CS, the word extracting unit 321 acquires a document set Dall including all the documents belonging to the category set CS.

  When it is determined in step S15 that processing has been executed for all categories, the word extraction unit 321 obtains text information (document names and texts included in the document) of all documents included in the acquired document set Dall. Morphological analysis is performed (step S16). As a result, the word extraction unit 321 extracts words (morphemes) included in each of the documents included in the document set Dall.

  Next, the word extraction part 321 performs the process of the following steps S17-S21 about each of the extracted word. A word to be processed in steps S17 to S21 is referred to as a word t.

  In this case, the word extraction unit 321 generates tf (t, Dall) indicating the number of words t appearing in the document set Dall (word frequency of the word t in the document set Dall) and the word t appears in the document set Dall. Df (t, Dall) indicating the number of documents to be processed (document frequency of the word t in the document set Dall) is calculated (step S17).

  The word extraction unit 321 stores the calculated tf (t, Dall) and df (t, Dall) in, for example, a storage unit (not shown) inside the analysis axis generation unit 32 (hereinafter referred to as a word data storage unit) ( Step S18).

  Next, the word extraction unit 321 executes the following processes of steps S19 and S20 for the category Ci included in the category set CS.

  In this case, the word extraction unit 321 includes tf (t, Di) indicating the number of words t appearing in the document set Di (document set belonging to the category Ci) (word frequency of the word t in the document set Di) and the document Df (t, Di) indicating the number of documents in which the word t appears in the set Di (document frequency of the word t in the document set Di) is calculated (step S19).

  The word extraction unit 321 stores the calculated tf (t, Di) and df (t, Di) in the word data storage unit (step S20).

  Next, the word extraction unit 321 determines whether or not the processing in steps S19 and S20 has been executed for all categories included in the category set CS (that is, categories C1, C2,..., Ca) (step S21). .

  If it is determined that the process has not been executed for all categories (NO in step S21), the process returns to step S19 described above and the process is repeated.

  On the other hand, when it is determined that the processing has been executed for all categories (YES in step S21), the word extraction unit 321 determines whether the processing in steps S17 to S21 has been executed for all the extracted words. (Step S22).

  If it is determined that the processing has not been performed for all words (NO in step S22), the process returns to the above-described step S17 and the processing is repeated. In this case, the processing is executed with the word t for which the processing of steps S17 to S21 has not been executed as the word t.

  On the other hand, when it is determined that the process has been executed for all words (YES in step S22), the word extraction process is terminated.

  When the word extraction process is performed in this way, the word data storage unit belongs to the category Ci for each category Ci (set) included in the word t extracted by the word extraction unit 321 and the category set CS. A word frequency TF (tf (t, Di)) and a document frequency DF (df (t, Di)) of the word t in the document set Di are stored. The word data storage unit stores the word frequency TF (tf (t, Dall)) and document frequency of the word t in the document set Dall belonging to the category set CS for each word t extracted by the word extraction unit 321. DF (df (t, Dall)) is stored.

  In the word extraction process illustrated in FIG. 13, it has been described that the processes in steps S <b> 17 to S <b> 21 are performed on all the words extracted by the word extraction unit 321. The target words may be narrowed down by removing unnecessary words from the extracted words. Specifically, the configuration may be such that only the words whose part of speech is a noun including sa-variant nouns and proper nouns are considered, and the conjunctions, adverbs and the like are not subject to processing. Further, when the document stored in the document storage unit 22 is a patent document, for example, words such as “device” and “means” do not represent the characteristics of the document and can be removed as unnecessary words. I do not care.

  Here, FIG. 14 shows an example of the data structure of the word data storage unit after the word extraction processing is executed.

  As shown in FIG. 14, the word data storage unit stores the document frequency DF and word frequency TF of the word in association with the word extracted by the word extraction unit 321 and the category (set) included in the category set CS. Stored. In the example shown in FIG. 14, for convenience, only “operation”, “communication”, “panel”, “range”, “film”, and “display” are shown as words extracted by the word extraction unit 321. Only the categories “Company A”, “Company B”, “Company C”, and “Company D” are shown as categories included in the category set CS.

  Specifically, in the word data storage unit, the document frequency DF (the word “operation” is the word t and the document set belonging to the category “Company A”) is associated with the word “operation” and the category “Company A”. “34” is stored as df (t, Di)) when is a document set Di. This shows that the word “operation” appears in 34 documents in the document set belonging to the category “Company A”.

  Similarly, the word data storage unit associates the word “operation” and the category “Company A” with the word frequency TF (the word “operation” is the word t, and the document set belonging to the “Company A” category is a document. “117” is stored as tf (t, Di)) in the case of the set Di. This shows that the word “operation” appears 117 times in the document set belonging to the category “Company A”.

  Although only the document frequency DF and the word frequency TF stored in the word data storage unit in association with the word “operation” and the category “Company A” have been described here, other document frequencies DF and word frequencies TF are also described. Since it is the same, the detailed description is abbreviate | omitted.

  Further, the word data storage unit stores the document frequency DF and the word frequency TF in all documents (document set Dall belonging to the category set CS) of each word extracted by the word extraction unit 321.

  In the example illustrated in FIG. 14, “169” is stored in the word data storage unit as the document frequency DF of the word “operation” in all documents (df (t, Dall) when the word “operation” is the word t). Stored. This indicates that the word “operation” appears in 169 documents in the document set Dall belonging to the category set CS.

  Similarly, the word data storage unit stores “525” as the word frequency TF of the word “operation” in all documents (tf (t, Dall) when the word “operation” is the word t). . This shows that the word “operation” appears 525 times in the document set Dall belonging to the category set CS.

  Although not shown in FIG. 14, the word data storage unit stores the number of documents belonging to the category for each category included in the category set CS (that is, the number of documents included in the document set Di belonging to the category Ci. Number) and the number of documents belonging to the category set CS (that is, the number of documents included in the document set Dall belonging to the category set CS) are stored.

  Next, the procedure of the feature word extraction process (the process of step S5 shown in FIG. 10) executed by the feature word extraction unit 322 will be described with reference to the flowchart of FIG. The information stored in the word data storage unit is referred to as word data for convenience.

  First, the feature word extraction unit 322 acquires the word data stored in the word data storage unit (step S31).

  Next, the feature word extraction unit 322 performs the following steps S32 and S33 for each word in the acquired word data. A word to be processed in steps S32 and S33 is referred to as a word t.

  The feature word extraction unit 322 calculates the feature degree of the word t with respect to the analysis axis category Ctgt (category set CS) using the acquired word data (step S32).

  Here, calculation of the characteristic degree of the word t with respect to the analysis axis category Ctgt will be described in detail. In the following description, the set of all documents belonging to the category set CS (analysis axis category Ctgt) is referred to as the document set Dall as described above. Further, it is assumed that the category set CS includes categories C1, C2,..., Ca, and a is the number of categories included in the category set CS. Further, the i-th category among the categories included in the category set CS is defined as a category Ci (i = 1, 2,..., A). A set of documents belonging to the category Ci is set as a document set Di. Further, the number of documents included in the document set Dall is set as the document number | Dall |, and the number of documents included in the document set Di is set as the number of documents | Di |.

First, the feature word extraction unit 322 determines the occurrence probability of the word t in the document set Dall based on the number of documents | Dall | and the document frequency DF of the word t in the document set Dall (that is, df (t, Dall)). calculating the degree of freedom phi _t deviation s _t and the bias s _t of the each document. Specifically, the bias s _t and the degree of freedom φ _t are calculated by the following equations (1) and (2).

Also, the feature word extraction unit 322 is based on the number of categories a included in the category set CS, the number of documents | Di |, and the document frequency DF of the word t in the document set Di (that is, df (t, Di)). A bias s _a for each category with respect to the appearance probability of the word t in the document set Dall and _a degree of freedom φ _a of the bias s _a are calculated. Specifically, the bias s _a and the degree of freedom φ _a are calculated by the following equations (3) and (4).

Next, the feature word extraction unit 322 calculates the average for each category based on the bias s _t , the bias s _a calculated as described above, the number of documents | Dall |, and the number of categories a included in the category set CS. calculating the degree of freedom phi _e deviation _{s e} and the bias _{s e} against. Specifically, the bias s _e and the degree of freedom φ _e are calculated by the following equations (5) and (6).

Furthermore, the feature word extraction section 322, the calculated deviation s _a as described _above, the degree of freedom phi _a, on the basis of the deviation s _e and freedom phi _e, and variance v _a between categories in the category set CS to calculate the variance _{v e} of error. Specifically, the dispersion v _e of dispersion v _a and error between categories is calculated by the following equation (7) and (8).

Finally, feature word extraction section 322, based on the variance _{v a} and error of variance _{v e} between categories, which are calculated as described above, calculates the term t features degree score (t, CS) for category set CS To do. Specifically, the feature score (t, CS) is calculated by the following equation (9).

  In comparing the categories included in the category set CS, words that appear biased to a specific category in the category set CS to be compared are important. For this reason, the feature word extraction unit 322 applies the analysis of variance method as described above, and calculates the significance of the bias in the appearance probability between categories for the category set CS (analysis axis category Ctgt) as the feature degree of the word t. Is done.

  When the process of step S32 is executed, the feature word extraction unit 322 stores the calculated feature degree of the word t in the word data storage unit (step S33).

  Next, the feature word extraction unit 322 determines whether or not the processes of steps S32 and S33 have been executed for all words in the word data (step S34).

  If it is determined that the processing has not been executed for all words (NO in step S34), the process returns to the above step S32 and the processing is repeated. In this case, the processing is executed with the word t for which the processing of steps S32 and S33 has not been executed.

  As described above, by repeating the processes of steps S32 and S33 for all the words in the word data, the feature degree is calculated for each of the words, and the feature degree is stored in the word data storage unit for each word. .

  Here, FIG. 16 shows an example of the data structure of the word data storage unit after the feature degree calculated for each word in the word data is stored.

  As illustrated in FIG. 16, the word data storage unit stores a feature degree for each word in the word data.

  In the example illustrated in FIG. 16, for example, “1.50” is stored as the degree of feature of the word “operation” in the word data storage unit.

  Although the word “operation” has been described here, the word data storage unit similarly stores the degree of feature for other words.

  When it is determined in step S34 that the processing has been executed for all the words, the feature word extraction unit 322 refers to the feature degree stored for each word in the word data storage unit, and from among the words in the word data. A feature word for the analysis axis category Ctgt (category set CS) is extracted (step S25).

  In this case, the feature word extraction unit 322 extracts, for example, words with the highest degree of feature among the words in the word data as feature words. Here, n is, for example, the number specified by the user in the column 111c provided in the sub screen 111 of the feature word extraction request screen 110 shown in FIG. Note that n may be set in advance in the document analysis apparatus 30, for example.

  Further, the feature word extraction unit 322 may be configured to extract a word whose feature degree is equal to or greater than a predetermined value (threshold value) as a feature word.

  Thus, the feature words for the category set CS extracted by the feature word extraction unit 322 are presented to the user as described above. In this case, in addition to the feature word extracted by the feature word extraction unit 322, the feature in the document set belonging to each category included in the category set CS (that is, a category positioned below the analysis axis category Ctgt in the hierarchical structure). Both the word document frequency DF and the like may be presented.

  As described above, in the present embodiment, a word is extracted from a document set belonging to the analysis axis category Ctgt (category set CS including a category positioned under the analysis axis) specified by the user, and the word appears in the document set. Document frequency indicating the number of documents to be performed (first document number) and document frequency indicating the number of documents in which the word appears in a document set belonging to a category positioned below the analysis axis category Ctgt in the hierarchical structure (second document) The number of documents) is calculated, the number of documents included in the document set belonging to the analysis axis category Ctgt, the number of categories located below the analysis axis category Ctgt, the calculated document frequency (first and second) Based on the number of documents), the feature degree of the word for the analysis axis category Ctgt is calculated, and based on the calculated feature degree of the word Extracting the words as characteristic words for analysis axis category Ctgt.

  As described above, in the present embodiment, by applying the method of analysis of variance, the significance of the bias in the appearance probability between categories of each word is calculated as the word feature degree for the analysis axis category Ctgt (category set CS). In order to extract feature words for the analysis axis category Ctgt based on the calculated feature degree, a plurality of categories (a plurality of categories included in the category set CS) positioned below the analysis axis category Ctgt in the hierarchical structure. It is possible to extract feature words suitable for comparing contents with each other.

(Second Embodiment)
Next, a second embodiment will be described with reference to FIG. FIG. 17 is a block diagram mainly showing a functional configuration of the document analysis apparatus according to the present embodiment. The same parts as those in FIG. 2 described above are denoted by the same reference numerals, and detailed description thereof is omitted. Here, parts different from FIG. 2 will be mainly described.

  The hardware configuration of the document analysis apparatus according to this embodiment is the same as that of the first embodiment described above, and will be described with reference to FIG. 1 as appropriate.

  In the present embodiment, a category to which a document belongs (hereinafter referred to as a feature word category) is generated using the feature words extracted by the processing described in the first embodiment, and specified by the user in the hierarchical structure. This is different from the first embodiment described above in that cross tabulation is performed with a category located under the category (analysis axis category Ctgt) and the feature word category.

  As shown in FIG. 17, the document analysis apparatus 50 according to the present embodiment includes a user interface unit 51 and an analysis axis generation unit 52. In the present embodiment, these units 51 and 52 are realized by the computer 10 shown in FIG. 1 executing the program 21 stored in the external storage device 20.

  The user interface unit 51 includes a cross tabulation unit 511. The cross tabulation unit 512 cross tabulates the analysis axis category Ctgt and the feature word category. The feature word category is a category generated using a feature word for the analysis axis category Ctgt as will be described later.

  The cross tabulation result by the cross tabulation unit 511 is presented to the user by the user interface unit 51. In the cross tabulation result, the number of documents belonging to both the category (category having the analysis axis category Ctgt as the upper category) and the feature word category in the hierarchical structure (the third document number) ) Is included.

  The analysis axis generation unit 52 includes a feature word characteristic calculation unit 521 and a feature word category generation unit 522.

  The feature word characteristic calculation unit 521 calculates the commonality of the feature words extracted by the feature word extraction unit 322 with respect to the analysis axis category Ctgt (category set CS including a category positioned below the analysis target category Ctgt in the hierarchical structure). To do. The commonality of the feature words with respect to the analysis axis category Ctgt is used as a scale representing, for example, common points / differences in the analysis axis category Ctgt (category set CS). The commonality of feature words for the analysis axis category Ctgt is the number of documents belonging to the category set CS, the number of documents belonging to each category included in the category set CS, and the document frequency of the feature words in the document set belonging to the category set CS ( (First document number) and the document frequency (second document number) of the feature word in the document set belonging to the category included in the category set CS.

  The feature word category generation unit 522 uses the feature word extracted by the feature word extraction unit 322 to generate a category (feature word category) having the feature word as a category name. A document including the feature word belongs to the feature word category generated using the feature word. In this case, the feature word category generation unit 522 generates feature word categories using the feature words in descending order of the commonality of the feature words calculated by the feature word characteristic calculation unit 521, for example. The feature word category generation unit 522 stores category information related to the generated feature word category in the category storage unit 23.

  Next, a processing procedure of the document analysis apparatus 50 according to the present embodiment will be described with reference to the flowchart of FIG.

  First, the processes of steps S41 to S46 corresponding to the processes of steps S1 to S6 shown in FIG. 10 described above are executed.

  Next, the feature word characteristic calculation unit 521 included in the analysis unit generation unit 52 represents common points / differences in the analysis axis category Ctgt (category set CS) for the feature words extracted by the feature word extraction unit 322. A process of calculating a common degree as a scale (hereinafter referred to as a feature word characteristic calculation process) is executed (step S47). This feature word characteristic calculation process is executed based on the word data stored in the word data storage unit described above. Details of the feature word characteristic calculation process will be described later.

  When the feature word characteristic calculation process is executed, the feature word category generation unit 522 generates a feature word category (the category of the feature word) using each of the feature words extracted by the feature word extraction unit 322 ( Hereinafter, a feature word category generation process is described (step S48). In this feature word category generation process, for example, categories having the feature words as category names (that is, feature word categories) are generated in descending order of the commonality of the feature words calculated by the feature word characteristic calculation unit 521. Note that the feature word category generated by the feature word category generation unit 522 is a category positioned at a lower level of the intermediate category arranged for convenience in the hierarchical structure as will be described later.

  The category information related to the feature word category generated by the feature word category generation unit 522 is stored in the category storage unit 23. Details of the feature word category generation process will be described later.

  When the feature word category generation process is executed, for example, the category presenting unit 311 included in the user interface unit 51, based on the category information stored in the category storage unit 23, the category of the category to which the feature word category is added. Present (display) the hierarchical structure to the user.

  Here, FIG. 19 shows an example of a screen (category hierarchical structure presentation screen) when a category hierarchical structure is presented by the category presenting unit 311.

  As shown in FIG. 19, on the category hierarchical structure presentation screen 200, category names of categories constituting the hierarchical structure are displayed on a tree (category tree).

  In the category hierarchical structure presentation screen 200 shown in FIG. 19, a feature word category is added as compared with the categories displayed on the category hierarchical structure presentation screen 100 shown in FIG.

  Specifically, the category hierarchical structure presentation screen 200 indicates that the category ““ Applicant ”analysis axis” is a category positioned under the root category. In the category hierarchical structure presentation screen 200, a plurality of categories including the categories “display”, “operation”, “cooking”, “system”, and “communication” are positioned below the category ““ Applicant ”analysis axis”. It is shown that it is a category.

  A plurality of categories including categories “display”, “operation”, “cooking”, “system”, and “communication” on the category hierarchical structure presentation screen 200 are the feature word categories generated by the feature word category generation unit 522. . The categories “display”, “operation”, “cooking”, “system”, and “communication” include the feature words “display”, “operation”, “cooking”, “system”, and “communication” extracted by the feature word extraction unit 322. This is a feature word category generated using “communication”.

  Here, when the category hierarchical structure presentation screen 200 is presented by the category presentation unit 311, the user is displayed on the category hierarchical structure presentation screen 200 by referring to the category hierarchical structure presentation screen 200. Two categories (hereinafter referred to as cross tabulation target categories Ctgt1 and 2) that are cross tabulation targets (analysis axes) can be designated from a plurality of categories. Here, as the cross tabulation target categories Ctgt1 and 2, the analysis axis category Ctgt and the category (intermediate category) positioned higher than the feature word category in the hierarchical structure are specified.

  Assuming that, for example, the categories “by applicant” and ““ applicant ”analysis axis” are designated as the cross tabulation target categories Ctgt1 and 2 on the category hierarchical structure presentation screen 200, a screen as shown in FIG. 210 is displayed.

  As shown in FIG. 20, the cross tabulation request screen 210 displays a sub screen 211 in addition to the hierarchical structure of categories. In this sub-screen 211, for example, a 2-axis map is displayed with "Category" By Applicant "and" "Applicant" Analysis Axis ". Select the category for the vertical axis. Is displayed. By confirming such a sub-screen 211, the user can select a category as a vertical axis in the cross tabulation among the designated categories “by applicant” and ““ applicant ”analysis axis”.

  The sub-screen 211 is provided with an execution button 211a and a cancel button 211b.

  When the execution button 211a provided on the sub-screen 211 is selected in response to a user operation on the document analysis apparatus 50, the cross tabulation target categories Ctgt1 and 2 (here, categories “by applicant” and ““ applicant ”) are selected. Cross analysis of “Analysis axis”) is requested to the document analysis apparatus 50.

  On the other hand, when a cancel button 211b provided on the sub screen 211 is selected in response to a user operation on the document analysis apparatus 50, a screen for reselecting the cross tabulation target categories Ctgt1 and 2 is shown in FIG. 19, for example. A category hierarchical structure presentation screen 200 is displayed.

  Returning to FIG. 18 again, it is determined whether or not the above-described cross tabulation is requested according to the user's operation (step S49).

  When it is determined that the cross tabulation is requested (YES in step S49), the cross tabulation unit 511 included in the user interface unit 51 includes the cross tabulation target category Ctgt1 designated by the user on the category hierarchy structure presentation screen 200 described above. Cross tabulation processing for 2 is executed (step S50). In this cross tabulation process, for example, a two-axis map (cross tabulation result) is generated (acquired) with the cross tabulation target category Ctgt1 as the vertical axis and the cross tabulation target category Ctgt2 as the horizontal axis. This cross tabulation result includes both a category (category included in the category set CS) located below the cross tabulation target category Ctgt1 and a category (feature word category) located below the cross tabulation target category Ctgt2 in the hierarchical structure. Contains the number of documents to which it belongs. The details of the cross tabulation process and the cross tabulation result will be described later.

  When the cross tabulation process is executed, the user interface unit 51 presents the cross tabulation result generated (acquired) by the cross tabulation unit 511 to the user (step S51).

  As described above, by specifying the category one level higher than the analysis target category Ctgt and the feature word category generated by the feature word category generation unit 522 as the cross tabulation target categories Ctgt1 and 2, the analysis target category Ctgt is assigned to the analysis target category Ctgt. On the other hand, it is possible to present to the user a cross tabulation result that makes the commonality and difference of feature words clear.

  Next, a processing procedure of the feature word characteristic calculation process (the process of step S47 shown in FIG. 18) executed by the above-described feature word characteristic calculation unit 521 will be described with reference to the flowchart of FIG.

  Here, the feature word extracted by the feature word extraction unit 322 is a significant word with respect to the analysis axis category Ctgt (category set CS including a category positioned below the analysis axis category Ctgt), but is common to the analysis axis category Ctgt. Feature words representing points and feature words representing differences are mixed. Therefore, in the feature word characteristic calculation process, an index called “commonness” is calculated as a scale representing common points / differences in the analysis axis category Ctgt.

  Note that a feature word representing a common point in the analysis axis category Ctgt (category set CS) is a feature word having a significantly high appearance probability in a plurality of categories included in the category set CS. Specifically, when the analysis axis category Ctgt is the category “applicant” in the patent document, for example, the company that is the applicant (that is, the categories “A company” and “B company” that are positioned below the analysis axis category Ctgt). , Etc.) is a feature word that represents a common point in the analysis axis category Ctgt.

  On the other hand, a feature word representing a difference in the analysis axis category Ctgt (category set CS) is a feature word having a significantly high appearance probability in a specific category (one or a small number of categories) included in the category set CS. . Specifically, as described above, when the analysis axis category Ctgt is the category “applicant” in the patent document, the company that is the applicant (category “Company A”, “B” located under the analysis axis category Ctgt). A feature word representing a difference (that is, a technology that only a specific company is working on intensively) is a feature word representing a difference in the analysis axis category Ctgt.

  First, the feature word characteristic calculation unit 521 acquires a feature word set (hereinafter referred to as a feature word set KS) including the feature words extracted by the feature word extraction unit 322 (step S61).

  Next, the feature word characteristic calculation unit 521 executes the following steps S62 and S63 for each of the feature words included in the acquired feature word set KS. The feature word to be processed in steps S62 and S63 is referred to as a feature word k.

  The feature word characteristic calculation unit 521 calculates the commonality of the feature word k with respect to the analysis axis category Ctgt (category set CS) (step S62). The degree of commonality of the feature word k is used as a scale representing the common point / difference in the analysis axis category Ctgt as described above.

  Here, the commonality of the feature word k is the number of documents belonging to the category set CS, the number of documents belonging to each category included in the category set CS, and the number of documents in which the feature word k appears in the document set belonging to the category set CS. The number and the number of documents in which the feature word k appears in the document set belonging to each category included in the category set CS are calculated. Specifically, the commonality of the feature word k is calculated by the following equation (10).

In the following description, a set of documents belonging to the category set CS is referred to as a document set Dall. Further, the number of categories included in the category set CS is a, the i-th category among the categories included in the category set CS is the category Ci (i = 1, 2,... A), and the documents belonging to the category Ci Let the set be a document set Di.

Note that c (k, Di, Dall) in equation (10) is defined as in equation (11) below.

  In this equation (11), df (k, Di) is the document set Di (i = 1, 2,..., A) belonging to the category Ci (i = 1, 2,..., A) included in the category set CS. This is the number of documents in which the feature word k appears (that is, the document frequency of the feature word k in the document set Di). Df (k, Dall) is the number of documents in which the feature word k appears in the document set Dall belonging to the category set CS (that is, the document frequency of the feature word k in the document set Dall). Note that df (k, Di) and df (k, Dall) are acquired from the word data stored in the word data storage unit as described above.

  In the equation (11), | Di | is the number of documents included in the document set Di (that is, the number of documents belonging to the category Ci). Similarly, | Dall | is the number of documents included in the document set Dall (that is, the number of documents belonging to the category CS).

  In Expression (11), γ is a threshold for calculating the degree of commonality of the feature word k, and is specified as a magnification with the appearance probability in the entire document set Dall. Note that this γ (value) may be set in advance in the document analysis apparatus 50, or may be specified by the user every time the feature word characteristic calculation process is performed.

  As the degree of commonality of the feature word k calculated as described above is larger, the feature word k is a feature word that better represents a common point in the analysis axis category Ctgt (category set CS). On the other hand, the smaller the commonality of the feature word k, the more the feature word k is a feature word that better represents the difference in the analysis axis category Ctgt (category set CS).

  When the process of step S62 is executed, the feature word characteristic calculation unit 521 stores the calculated commonality of the feature word k in the word data storage unit (step S63).

  Next, the feature word characteristic calculation unit 321 determines whether or not the processing of Step S62 and Step S63 has been executed for all feature words included in the feature word set KS (Step S64).

  If it is determined that the process has not been executed for all feature words (NO in step S64), the process returns to the above-described step S62 and is repeated. In this case, the process is executed with the feature word for which the processes of steps S62 and S63 have not been executed as the feature word k.

  As described above, by repeating the processes of steps S62 and S63 for all feature words included in the feature word set KS, the degree of commonality is calculated for each of the feature words, and the feature degree is word data for each feature word. Stored in the storage unit.

  Here, FIG. 22 shows an example of the data structure of the word data storage unit after the degree of commonality calculated for each feature word is stored. Here, it is assumed that “operation”, “communication”, “panel”, “film”, and “display” among the words in the word data stored in the word data storage unit are characteristic words.

  As shown in FIG. 22, the word data storage unit stores the commonality for each feature word. In the example illustrated in FIG. 22, for example, “6” is stored in the word data storage unit as the commonality of the feature word “operation”. In the word data storage unit, the degree of commonness is similarly stored for other feature words.

  Also, as described above, since the degree of commonality is calculated for the feature word, for example, the degree of commonality with respect to the category set CS is not stored (calculated) for the word “range” not extracted as the feature word. Therefore, as shown in FIG. 22, the item of “commonness” of “range” is empty.

  When it is determined in step S64 that the processing has been executed for all the feature words, the feature word characteristic calculation unit 521 refers to the word data stored in the word data storage unit (commonness for each feature word), The feature words included in the feature word set KS are sorted in the degree of commonality (descending order) (step S65). For example, in the example shown in FIG. 22 described above, the feature words included in the feature word set KS are sorted in the order of feature words “display”, “operation”, “communication”, “film”, and “panel”.

  When the process of step S65 is executed, the feature word characteristic calculation process ends. At this time, the feature word characteristic calculation unit 521 passes the feature word set KS including the feature words sorted by the commonality to the feature word category generation unit 522.

  Note that the feature words included in the feature word set KS passed to the feature word category generation unit 522 are sorted in descending order with respect to the degree of commonality. It is a feature word that better expresses the difference.

  Next, the processing procedure of the feature word category generation process (the process of step S48 shown in FIG. 18) executed by the above-described feature word category generation unit 522 will be described with reference to the flowchart of FIG. In the feature word category generation process, feature word categories are generated using the feature words sorted with the above-mentioned commonality.

  First, the feature word category generation unit 522 acquires the feature word set KS passed from the feature word characteristic calculation unit 521 (step S71). The feature word set KS includes the feature words sorted by the above-mentioned commonality (that is, the feature words in the order of commonality).

  Next, the feature word category generation unit 522 generates a category (hereinafter referred to as an intermediate category Cp) positioned higher than the feature word category generated in the feature word category generation process in the hierarchical structure (step S72). The intermediate category Cp generated here is an intermediate category for arranging the feature word categories in the hierarchical structure for convenience and designating the feature word category for the cross tabulation process as described above. This intermediate category Cp is assumed to be a category positioned below the root category in the hierarchical structure.

  The feature word category generation unit 522 stores the category information regarding the generated intermediate category Cp in the category storage unit 23 (step S73). In this case, the feature word category generation unit 522 includes a category number for identifying the intermediate category Cp, an upper category number for identifying a root category positioned above the intermediate category Cp, a category name of the intermediate category Cp, and an intermediate category. The category information including the classification rule indicating the document belonging to Cp is stored in the category storage unit 23.

  Here, FIG. 24 shows an example of the data structure of the category information related to the intermediate category Cp. As shown in FIG. 24, the category information 236 includes a category number “C003”, a higher category number “C000”, a category name ““ Applicant ”classification axis”, and a classification rule “true (constant)”.

  The category number “C003” is an identifier for uniquely identifying the intermediate category Cp, and is automatically issued when the intermediate category Cp is generated by the feature word category generation unit 522, for example.

  The upper category number “C000” is a category number for identifying a root category positioned higher than the intermediate category Cp in the hierarchical structure.

  The category name ““ Applicant ”classification axis” is the category name of the intermediate category Cp. The category name of the intermediate category Cp may be automatically determined based on, for example, the category name of the analysis target category Ctgt (for example, the category name “applicant”) or may be specified by the user. .

  The classification rule “true (constant)” indicates a document (condition) belonging to the intermediate category Cp. According to this classification rule “true (true)”, as described above, it is indicated that all documents satisfy the condition. For example, a classification rule similar to the classification rule included in the category information related to the analysis target category Ctgt may be automatically set as the classification rule indicating the document belonging to the intermediate category Cp.

  Here, the intermediate category Cp has been described as being a category positioned below the root category, but the intermediate category Cp may be a category positioned below the analysis target category Ctgt, for example.

  Returning to FIG. 23 again, the feature word category generation unit 522 executes the following processes of steps S74 and S75 for each of the feature words included in the acquired feature word set KS. Note that the feature word category generation unit 522 performs processing in the order of feature words included in the feature word set KS (that is, the order sorted in descending order by commonality). The feature word to be processed in steps S74 and S75 is referred to as feature word k.

  In this case, the feature word category generation unit 522 generates a feature word category (hereinafter referred to as a feature word category KC) using the feature word k (step S74). Note that the feature word category KC is a category positioned below the intermediate category Cp described above in the hierarchical structure.

  Next, the feature word category generation unit 522 stores category information related to the generated feature word category KC in the category storage unit 23 (step S75). In this case, the feature word category generation unit 522 includes a category number for identifying the feature word category KC, a higher category number for identifying the intermediate category Cp positioned higher than the feature word category KC, and a category of the feature word category KC. The category information including the classification rule indicating the document belonging to the name and the feature word category KC is stored in the category storage unit 23.

  Note that the category name of the feature word category KC is, for example, the feature word k. A classification rule indicating a document belonging to the feature word category KC is a classification rule indicating a document including the feature word k, that is, a classification rule on condition that the feature word k is included. In other words, the feature word category KC generated by the feature word category generation unit 522 is a category to which a document including the feature word k in the text (for example, the body) belongs (is classified).

  Next, the feature word category generation unit 522 performs the processing of steps S74 and S75 up to the last feature word included in the feature word set KS as a result of executing the processing in the descending order sorted by the common degree as described above. It is determined whether it has been executed (step S76). That is, the feature word category generation unit 522 determines whether or not the process has been executed up to the feature word having the lowest degree of commonness among the feature words included in the feature word set KS.

  If it is determined that the process has not been executed up to the last feature word (NO in step S76), the process returns to step S74 described above and the process is repeated. In this case, in the feature words sorted by the degree of commonality, the process is executed with the feature word next to the feature word k (the feature word having the next highest degree of commonality) having undergone the processing of steps S73 and S74 as the feature word k. The

  As described above, when the processes of steps S74 and S75 are executed up to the last feature word sorted by the commonness included in the feature word set KS, the category information regarding the feature word categories generated in the order of commonality of the feature words is the category. The state is stored in the storage unit 23.

  If it is determined in step S76 that the process has been executed for the last feature word, the feature word category generation process ends.

  By executing the feature word category generation process in this manner, the analysis axis becomes the other analysis axis suitable for comparing the categories of analysis axes (analysis axis category Ctgt) designated by the user in the cross tabulation process described later. Categories can be generated.

  Here, with reference to FIG. 25 and FIG. 26, the category information regarding the feature word category stored in the category storage unit 23 will be specifically described.

  FIG. 25 shows an example of a data structure of category information related to a feature word category (hereinafter referred to as feature word category 1) generated using, for example, the feature word “display”.

  As shown in FIG. 25, the category information 237 includes a category number “C301”, a higher category number “C003”, a category name “display”, and a classification rule “contains (./text,“ display ”)”.

  The category number “C301” included in the category information 237 is an identifier for uniquely identifying the feature word category 1, and is automatically issued when, for example, the feature word category 1 is generated by the feature word category generation unit 522. Is done.

  The upper category number “C003” included in the category information 237 is a category number for identifying the intermediate category Cp (the intermediate category generated in step S72 shown in FIG. 23) positioned higher than the feature word category 1.

  The category name “display” included in the category information 237 is the category name of the feature word category 1. As described above, the feature word “display” is used as the category name of the feature word category 1.

  Further, the classification rule “contains (./text,“ display ”)” included in the category information 237 indicates a document (condition) belonging to the feature word category 1. According to this classification rule “contains (./ text,“ display ”)”, it is indicated that a document whose feature word “display” is included in the text belongs to the feature word category 1.

  FIG. 26 shows an example of a data structure of category information related to a feature word category (hereinafter referred to as feature word category 2) generated using, for example, a feature word “operation”.

  As shown in FIG. 26, the category information 238 includes a category number “C302”, a higher category number “C003”, a category name “operation”, and a classification rule “contains (./text,“ operation ”)”.

  The category number “C302” included in the category information 238 is an identifier for uniquely identifying the feature word category 2, and is automatically issued when, for example, the feature word category 2 is generated by the feature word category generation unit 522. Is done.

  The upper category number “C003” included in the category information 238 is a category number for identifying the intermediate category Cp positioned higher than the feature word category 2.

  The category name “operation” included in the category information 238 is the category name of the feature word category KC2. Note that the feature word “operation” is used as the category name of the feature word category 2 as described above.

  The classification rule “contains (./ text,“ operation ”)” included in the category information 238 indicates a document (condition) belonging to the feature word category 2. According to this classification rule “contains (./ text,“ display ”)”, it is indicated that a document whose feature word “operation” is included in the feature word category 2 belongs.

  Next, the processing procedure of the cross tabulation process (the processing of step S50 shown in FIG. 18) executed by the cross tabulation unit 511 described above will be described with reference to the flowchart of FIG.

  Here, as described above, the two categories to be subjected to cross tabulation (processing) designated by the user are referred to as cross tabulation target categories Ctgt1 and Ctgt2. The cross tabulation target category Ctgt1 is the analysis target category Ctgt (for example, the category “by applicant”), and the cross tabulation target category Ctgt2 is higher in the feature word category generated in the above-described feature word category generation processing. It is assumed that the intermediate category Cp is located (for example, the category “Applicant” classification axis ”). Further, it is assumed that the cross tabulation target category Ctgt1 is selected as the vertical axis on the sub-screen 211 of the cross tabulation request screen 210 shown in FIG.

  First, the cross tabulation unit 511 includes a category set (hereinafter, referred to as a category set CS1) including a category (a category having the cross tabulation target category Ctgt1 as an upper category) located in a lower level of the cross tabulation target category Ctgt1 in the hierarchical structure. A category set (hereinafter, referred to as a category set CS2) including a category (category having the cross-tabulation target category Ctgt2 as an upper category) positioned below the aggregation target category Ctgt2 is specified (step S81).

  Note that the categories included in the category set CS1 (categories positioned below the cross tabulation target category Ctgt1) are category numbers included in the category information including the category number for identifying the cross tabulation target category Ctgt1 as the upper category number. Is a category identified by. Similarly, the categories included in the category set CS2 (categories positioned below the cross tabulation target category Ctgt2) are determined by the category number of the category information including the category number for identifying the cross tabulation target category Ctgt2 as the upper category number. The category to be identified.

  Here, it is assumed that the category set CS1 includes a categories of categories C11, C12,..., C1a. Further, it is assumed that the category set CS2 includes b categories of categories C21, C22,..., C2b.

  Next, the cross tabulation unit 511 executes the processes of the following steps S82 to S86 for the category C1i (i = 1, 2,..., A) included in the identified category set CS1.

  In this case, the cross tabulation unit 511 acquires category information related to the category C1i (step S82).

  Next, the cross tabulation unit 511 executes the processes of steps S83 to S85 below for the category C2j (j = 1, 2,..., B) included in the identified category set CS2. Note that the categories C21 to C2b included in the category set CS2 are processed in the order generated by the above-described feature word category generation unit 522 (that is, in order of commonality), for example.

  In this case, the cross tabulation unit 511 acquires category information related to the category C2j (step S83).

  The cross tabulation unit 511 selects a document set Dcmn (C1i, C2j) including documents belonging to both the categories C1i and C2j based on the category information included in the acquired category information regarding the category C1i and category information regarding the category C2j. Obtain (step S84).

  The cross tabulation unit 511 uses the value of the i row and j column (the cell) of the cross tabulation result as the number of documents included in the document set Dcmn (C1i, C2j) (hereinafter, the number of documents | Dcmn (C1i, C2j) | Notation) (step S85).

  Next, the cross tabulation unit 511 determines whether or not processing has been executed for all categories (that is, categories C21, C22,..., C2b) included in the category set CS2 (step S86).

  If it is determined that processing has not been performed for all categories included in category set CS2 (NO in step S86), the process returns to step S83 described above and is repeated.

  On the other hand, when it is determined that processing has been executed for all categories included in the category set CS2 (YES in step S86), the cross tabulation unit 511 displays all categories included in the category set CS1 (that is, categories C11 and C12). ,..., C1a) is determined (step S87).

  If it is determined that processing has not been performed for all categories included in category set CS1 (NO in step S87), the process returns to step S82 described above and is repeated.

  On the other hand, when it is determined that the process has been executed for all categories included in the category set CS1 (YES in step S87), the cross tabulation process ends.

  By performing the cross tabulation process in this way, the number of documents belonging to both the category (C1i) included in the category set CS1 and the category (C2j) included in the category set CS2 (number of documents | Dcmn (C1i, C2j ) |) A cross tabulation result including |) can be obtained.

  The cross tabulation result obtained by the cross tabulation process is presented to the user by the user interface unit 51.

  Here, FIG. 28 shows an example of the cross tabulation result presented to the user by the user interface unit 51. Here, as described above, the cross tabulation target category Ctgt1 is the category “by applicant” (that is, the category set CS1 is a set of categories “A company” to “J company” located under the category “by applicant”. ) And the cross tabulation target category Ctgt2 is the category “Applicant” classification axis ”(that is, the categories“ display ”,“ operation ”,“ system ”,“ communication ”,“ Resin, film, plastic, panel, liquid crystal, composition, optics, polymerization, packaging, polarization, and cell). .

  As shown in FIG. 28, in the cross tabulation result, the values of the cells associated with the category (C1i) included in the category set CS1 and the category (C2j) included in the category CS2 (that is, the category C1i and the category Bubbles (circles) having a size corresponding to the number of documents belonging to both C2j | Dcmn (C1i, C2j) |) are displayed.

  In the cross tabulation result, each category (C2j) included in the category set (CS2) positioned under the category “Applicant” classification axis, which is the cross tabulation target category Ctgt2, is in the order of commonality (feature word generation In the order generated by the category portion 522). That is, the cross tabulation result is presented according to the degree of commonality with respect to the cross tabulation target category Ctgt1 (category designated by the user).

  By presenting such a cross tabulation result to the user, it is possible to visualize the common points or differences between the categories located under the categories to be cross tabulated. Common points and differences can be easily grasped.

  In the cross tabulation result shown in FIG. 28, category names (that is, feature words) of feature word categories on the horizontal axis (categories positioned under the category “Applicant” analysis axis ”) are arranged in order of commonality. Therefore, on the left side of the horizontal axis, feature words “display”, “operation”, “system”, etc. related to highly common technologies handled by many applicants (companies) are presented. The characteristic words “packaging”, “polarization”, “cell”, etc. relating to highly unique technologies that are intensively handled by the above are presented.

  Therefore, based on such cross tabulation results, for example, when the user looks at the analysis axis (category) “by applicant”, the user can more specifically grasp the technical tendency.

  In FIG. 28, the cross tabulation result is described as being presented as a bubble chart. However, for example, the value of each cell itself may be presented as the cross tabulation result, or a bar graph or the like may be presented. .

  As described above, in the present embodiment, a feature word category is generated using the feature words extracted by the feature word extraction unit 322, and the category located below the category specified by the user and the generated feature words A cross tabulation result including the number of documents belonging to both categories is presented to the user. In the present embodiment, the commonality of feature words with respect to a category (analysis axis category Ctgt) designated by the user is calculated, and the feature word categories are arranged based on the calculated commonality of feature words. The total result is presented.

  Thus, in the present embodiment, a cross tabulation result (tabulation map) is presented so that common points and differences of feature words are clarified with respect to an analysis axis (category designated by the user) focused on by the user. Therefore, the user can efficiently perform an appropriate analysis on the analysis axis. In other words, in the present embodiment, by presenting the cross tabulation results based on the commonality of feature words, the common points or differences in the analysis axis designated by the user are clarified, and the comparison between the categories of the user is supported. be able to.

  In the present embodiment, the process is described as being executed as shown in FIG. 18 described above. However, for example, when a feature word category has already been generated, the hierarchical structure of the category in step S42. May be requested without the feature word extraction being requested.

  According to at least one embodiment described above, it is possible to provide a document analysis apparatus that can extract feature words suitable for comparing the contents of a plurality of categories with each other.

  Note that the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage. Moreover, various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, constituent elements over different embodiments may be appropriately combined.

  DESCRIPTION OF SYMBOLS 10 ... Computer, 20 ... External storage device, 22 ... Document storage part, 23 ... Category storage part, 30 ... Document analysis apparatus, 31, 51 ... User interface part, 32, 52 ... Analysis axis generation part, 311 ... Category presentation part 321 ... word extraction unit, 322 ... feature word extraction unit, 511 ... cross tabulation unit, 521 ... feature word characteristic calculation unit, 522 ... feature word category generation unit.

Claims (5)

Document storage means for storing a plurality of documents including words belonging to a plurality of categories constituting a hierarchical structure;
Category storage means for storing category information indicating documents belonging to the category for each category constituting the hierarchical structure;
Extract words included in the plurality of documents belonging to the first category designated by the user indicated by the category information stored in the category information storage means from the plurality of documents stored in the document storage means. Word extraction means to
Based on the category information stored in the category storage means, the first document number indicating the number of documents in which the extracted word appears in the plurality of documents belonging to the first category, and the hierarchical structure A document number calculating means for calculating a second document number indicating the number of documents in which the extracted word appears in a plurality of documents belonging to a second category located under the first category;
Based on the number of documents belonging to the first category, the number of documents belonging to the second category, the number of second categories, and the calculated first and second document numbers, Feature degree calculating means for calculating the feature degree of the extracted word with respect to a category;
A document analysis apparatus comprising: a feature word extraction unit that extracts the extracted word as a feature word for the first category based on the feature degree calculated by the feature degree calculation unit. Category generating means for generating a feature word category to which a document including a feature word extracted by the feature word extracting means among a plurality of documents belonging to the first category;
Storage processing means for storing category information indicating documents belonging to the feature word category generated by the category generation means in the category storage means;
Presentation that presents the user with a total result including a third document number indicating the number of documents belonging to both the second category and the feature word category based on the category information stored in the category storage means The document analysis apparatus according to claim 1, further comprising: means. The number of documents belonging to the first category, the number of documents belonging to the second category, and a plurality of documents belonging to the first category calculated by the document number calculation means are extracted by the feature word extraction means. Based on a first document number indicating the number of documents in which the feature word appears and a second document number indicating the number of documents in which the feature word appears in a plurality of documents belonging to the second category. A common degree calculating means for calculating the common degree of the feature word for one category;
The document analysis apparatus according to claim 2, wherein the presenting unit presents the total result based on the commonality of the feature words calculated by the commonality calculation unit. The feature degree calculating means includes:
Based on the number of documents belonging to the first category and the number of first documents calculated by the number-of-documents calculation unit, each document with respect to the appearance probability of the word extracted by the word extraction unit in the plurality of documents And a first calculating means for calculating the bias and the degree of freedom of the bias;
The word in the plurality of documents belonging to the first category based on the number of the second category, the number of documents belonging to the second category, and the second document number calculated by the document number calculating means Second calculating means for calculating a bias for each category with respect to the appearance probability of the word extracted by the extracting means and a degree of freedom of the bias;
The number of documents belonging to the first category, the number of the second category, the bias calculated by the first calculation unit and the degree of freedom of the bias, and the second calculation unit Based on the bias and the degree of freedom of the bias, third calculation means for calculating the bias for the average for each category and the degree of freedom of the bias;
Based on the bias calculated by the second calculation means and the degree of freedom of the bias, and the bias calculated by the third calculation means and the degree of freedom of the bias, the variance between the categories and the variance of the error are calculated. A fourth calculating means for calculating;
Fifth calculation means for calculating a feature degree of the word extracted by the word extraction means for the first category based on variance between categories and error variance calculated by the fourth calculation means; The document analysis apparatus according to claim 1, further comprising: Document storage means for storing a plurality of documents including words belonging to a plurality of categories constituting a hierarchical structure, and category storage means for storing category information indicating documents belonging to the category for each category constituting the hierarchical structure In a document analysis apparatus composed of an external storage device having a computer and a computer using the external storage device, a program executed by the computer,
In the computer,
Extract words included in the plurality of documents belonging to the first category designated by the user indicated by the category information stored in the category information storage means from the plurality of documents stored in the document storage means. And steps to
Based on the category information stored in the category storage means, the first document number indicating the number of documents in which the extracted word appears in the plurality of documents belonging to the first category, and the hierarchical structure Calculating a second document number indicating the number of documents in which the extracted word appears in a plurality of documents belonging to a second category located under the first category;
Based on the number of documents belonging to the first category, the number of the second category, and the calculated first and second document numbers, the characteristic degree of the extracted word with respect to the first category is calculated. A calculating step;
Extracting the extracted word as a characteristic word for the first category based on the calculated characteristic degree.

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