JP2007293685A - Device, program, and method for mining text - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To solve such a problem that a modification relation, which focuses on the relation of not less than three words from the aspect of wider-range of analysis, is not freely and interactively reviewed since the range of analysis by a user is restricted up to the relation of two words since conventional technique is for two dimensional mapping of a two-word modification relation focusing on the relation of the two words. <P>SOLUTION: A text mining device includes: a modification relation extracting means for extracting the modification relation of the not less than two words; a modification relation structure constituting means for classifying the words constituting the inter-word modification relation by category and constituting a modification relation network structure among the words included in the classified category; a display means for displaying the inter-word modification relation network structure; an input means for receiving the input of setting of a display condition; and a display object data operating means for indicating the setting of the display condition with respect to the displayed inter-word modification relation network structure. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a technique for analyzing linguistic information contained in a large amount of text data, extracting relationships between words and dependency relationships, and visualizing and displaying the results of text mining of these relationships.

  With the spread of the Internet, corporate advertisements and consumer opinions regarding products have been released as Web pages, and a large amount of customer feedback has been sent as document data by e-mail and Web. In addition, the digitization of documents is progressing in the enterprise, and the accumulation of documents required for quality control work such as design specifications and failure investigation reports is progressing. The importance of text mining devices for extracting useful information from these large amounts of stored text and utilizing it for business improvement is increasing.

Among such text mining devices, conventionally, when a user defines a group according to conditions such as text attribute data and full-text search, and designates a certain group, a word or dependency information is extracted from the text in the group. There is an apparatus provided with means for overviewing information in a text by extracting and displaying a list in which words or dependency information are ranked based on their appearance frequency and weight based on a statistical index.

For example, in Patent Document 1 shown below, when a certain group defined by the user is specified according to conditions such as attribute data and full text search of text, the word or dependency information extracted from the text in the corresponding group is displayed as an appearance frequency or IDF. (Inverted Document Frequency) A technique for displaying a list in which words or dependency information are ranked based on weights is disclosed. In addition, based on these statistical information, words and dependency information are arranged on a two-dimensional map using commonly used statistical analysis techniques such as correspondence analysis and principal component analysis. A technique for visualization is disclosed.

Further, as a method for arranging and displaying the relationship between words on a map, an information map creation technique as disclosed in Patent Document 2 is disclosed. In this patent document 2, a technique for displaying relations between words in a network format and thinning out arcs is disclosed.

Japanese Patent Laid-Open No. 2004-021445 (page 15, FIG. 1) JP 2004-21913 A (page 17, FIG. 1 to FIG. 2, page 18, FIG. 5)

  In the prior art disclosed in Patent Document 1 and Patent Document 2 described above, since the two-dimensional dependency relationship is a two-word dependency relationship focusing on the relationship between two words, the range of analysis of the user is a relationship up to two words. It was limited to. For this reason, there has been a problem that it is impossible to give a free and interactive overview to the dependency relationship that focuses on a relationship of three words or more from a wider viewpoint of analysis.

  Also, in the prior art, when many dependency relationships extracted by analyzing texts are displayed, it is not easy to grasp the contents of the texts, and the words to be focused on in the analysis are other insignificant words. There was a problem that it was difficult to find it because it was buried in the group.

  In addition, in the prior art, when displaying a dependency relationship focusing on a relationship of three words or more, if only a single network structure is obtained if it is displayed without considering the importance of the links constituting the network. However, there is a problem that it is not possible to easily browse from a summary display to a detailed display by changing the granularity of analysis.

  In addition, in the related art, the dependency relationship of three or more words to be noticed in the analysis is buried in other unnecessary links, and it is difficult to browse only the nodes and links to be noticed. there were.

  Further, in the related art, in the case of a dependency network structure in which low-frequency words or non-important words are mixed, there is a problem that it is difficult to analyze the dependency relationship centered on important words.

  Also, in the prior art, when similar words individually become nodes of the network structure, links are created as different nodes even though they are similar words, so the number of links increases and is complicated, There was a problem that it was difficult to grasp the contents.

  In addition, in the related art, there is a problem in that it is difficult to grasp the detailed contents in the vicinity of a high-frequency dependency relationship in the display content of the dependency relationship between two words.

  Further, in the related art, in the display contents of the dependency relationship between two words, since the dependency relationship of three words or more cannot be displayed, the content of the dependency relationship of a group of three words or more cannot be grasped. was there.

  In addition, in the prior art, since a user cannot specify any group of dependency groups as a composite concept, a new relationship between composite concepts is extracted from the set of dependency relationships arbitrarily specified as a composite concept and analyzed. There was a problem that important relationship information latent in the subject could not be analyzed.

  In addition, in the prior art, when the words constituting the dependency relationship are classified into groups, a knowledge source that depends on the field to be analyzed is not used as a classification criterion. There was a problem that it was difficult to grasp and it was difficult to perform analysis useful for the work to be analyzed.

  Also, in the prior art, it is impossible to interactively freely edit and modify the category to which each word belongs by referring to the dependency relationship of each word between displayed categories. There was a problem that it was not easy to grasp the contents of the relationship.

  The present invention has been made in order to solve the above-described problems, and includes a dependency relationship structure forming unit, whereby a dependency relationship between words extracted from an analysis target text is classified into a plurality of categories. From the overall overview of a wide range of dependency relationships spanning more than two words, to detailed refinement display, by enabling display and freely adjusting display information on categories, nodes, and arcs for the displayed network The purpose is to enable free and interactive analysis.

In order to solve such a problem, the text mining device, the text mining program, and the text mining method according to the present invention analyze the analysis target data, extract the word information extracted from the text of the analysis target data, and the analysis target A registration means for registering attribute values of data; a dependency extraction means for analyzing the extracted word information to extract a dependency relationship between two or more words; and a frequency of the dependency relationship between the words. Dependency frequency counting means for counting, attribute value frequency counting means for counting the frequency of the attribute value, and the word information extracted by the registration means and tabulated for the attribute value frequency by the attribute value frequency counting means Category-by-category word counting means for counting for each predefined category, and the words constituting the dependency relationship between the words as the category And a dependency relationship structure configuring unit that configures a dependency relationship network structure between words included in the classified category, a display unit that displays the dependency relationship network structure between the words, and a display condition Input means for receiving setting input and display target data operating means for instructing setting of the display condition for the displayed dependency relation network structure between words.

According to the present invention, it is possible to display a network in which the dependency relationships between words extracted from the text to be analyzed are classified into three or more categories by providing the dependency relationship structure constituting unit. The display information on categories, nodes, and arcs can be freely adjusted so that free interactive analysis can be performed from a broad overview of dependency relationships over three words to detailed refined display. Become.

Embodiment 1 FIG.
1 is a block diagram showing Embodiment 1 of the present invention. A registration unit 101 analyzes the analysis target data and registers word information extracted from the text of the analysis target data and the attribute value of the analysis target data in a word database 113 and an attribute database 112, which will be described later. Hereinafter, the database is referred to as DB. A dependency extraction unit 102 analyzes the extracted word information and extracts a dependency relationship between two or more words. Reference numeral 103 denotes an input unit that accepts input from the user. Reference numeral 104 denotes a display target data operation unit, which instructs to change data settings related to the dependency relationship network structure between displayed words. A control unit 105 controls a flow of a series of processes from registration of analysis target data to display. 106 is a dependency frequency totaling unit, which totals the frequency of dependency relationships between words extracted by the dependency extraction unit 102. Reference numeral 107 denotes an attribute value frequency totaling unit that counts the frequency of attribute values in the analysis target data. Reference numeral 108 denotes a category-specific word totaling unit that totalizes the words extracted by the registration unit 101 for each predefined category. Reference numeral 109 denotes a dependency relationship structure forming unit that classifies dependency relationships between words extracted from the text to be analyzed. Reference numeral 110 denotes a display unit that displays a dependency relationship network structure between words configured by the dependency relationship structure configuration unit 109. Reference numeral 111 denotes document data to be analyzed. An attribute DB 112 stores information related to attributes extracted from document data. Reference numeral 113 denotes a word DB, which stores information about words extracted from document data. Reference numeral 114 denotes a dependency DB, which stores information related to dependency relationships extracted from document data. A semantic category dictionary 115 stores semantic categories and words belonging to the semantic categories. Reference numeral 116 denotes output information from the display unit, which visually displays the dependency relationship structure.

  Hereinafter, an outline of basic processing in Embodiment 1 of the present invention will be described with reference to FIGS. 1 to 4 as appropriate. Details of the basic processing will be described with reference to the flowchart of the basic processing in the first embodiment of the present invention shown in FIG.

First, in step S201 of FIG. 2, the registration unit 101 reads the document data 111 to be analyzed, and extracts words appearing in the document data 111 and attribute information such as bibliographic information. Information about the extracted word is stored in the word DB 113, and information on the extracted attribute is stored in the attribute DB 112. FIG. 3 shows an example of the word DB 113 in the present invention. FIG. 3 shows an example in which word information such as a word ID, a headline, a part of speech, and a utilization form is stored in the word DB 113 after a word appearing in the document data 111 is extracted. As words to be stored, nouns, adjectives, adjective verbs and the like are selected as parts of speech to be extracted from, for example, a result of analysis by morphological analysis. FIG. 4 shows an example of the attribute DB 112 in the present invention. FIG. 4 shows an example in which document ID, reception date, type, target department, and the like are stored as document attributes. With respect to this attribute, various attribute types are designed and attribute values are given to each document data depending on the viewpoint of analysis of the document data 111 to be analyzed.

  Next, in step S202, the registration unit 101 calls the dependency extraction unit 102, extracts a dependency relationship between words appearing in the document data to be analyzed, and stores the relationship in the dependency DB 114. FIG. 5 shows an example of the dependency relationship in the present invention. In the figure, the word “A store / unkindness / attitude / heading” extracted by morphological analysis is obtained for the sentence example “A store, but unfriendly attitude comes to mind” (sentence ID = 1). It shows an example of extracting “unkind → attitude” and “attitude → head” which are two word dependency relationships. In this case, for the sentence with sentence ID = 1, the above-described dependency relationship of two words can be combined to simultaneously extract the dependency relationship of three words “unfriendly → attitude → head”. The dependency relationship obtained in this way is stored with a dependency source word, a dependency destination word, a sentence ID, etc. together with the dependency ID, as in the example of the dependency DB in the present invention of FIG.

  Next, in step S203, the control unit 105 receives the attribute information stored in the attribute DB 112 and sends the attribute information to the attribute value frequency totaling unit 106. The attribute value frequency totaling unit 106 determines the frequency of the attribute value for each attribute. Are counted.

  Next, in step S <b> 204, the control unit 105 receives word information stored in the word DB 112 and sends it to the category-specific word frequency totaling unit 108. The category-specific word frequency totaling unit refers to the word information received from the word DB 112 and the semantic category dictionary 115 storing the correspondence between the word classification and the semantic category, and the word frequency for each semantic category. Are counted.

  Next, in step S205, the control unit 105 receives the dependency information stored in the dependency DB 114, and the dependency frequency totaling unit 107 totals the frequency for each dependency relationship.

  Next, in step S206, a dependency relationship structure configuration process is performed. The dependency relationship structure configuration processing is executed by the dependency relationship structure configuration unit 109. Hereinafter, the operation of the dependency relationship structure constituent unit 109 will be described with reference to the detailed flowchart of FIG. First, in step S701, the dependency relationship structure configuring unit 109 acquires a dependency pair and a frequency for each dependency pair from the dependency frequency totaling unit 107.

  Next, in step S <b> 702, the category classification of words and the frequency of words belonging to each category are acquired from the category-specific word frequency totaling unit 108.

  Next, in step S703, link information between the words belonging to each category of the relation source and the words belonging to each category of the relation destination is generated for each category pair from the information acquired in steps S701 and S701. . At this time, a large value is set as a value associated with the link for a link with a large dependency frequency.

  Next, in step S704, in order to arrange and display the dependency relationship in a network form for each category, a display position is determined for each category and display information is generated. For example, when using a category part such as a general part of speech and subject-object as a category, the “verb” is placed at the center, and the “subject” is a subject with a “verb” on the left side. “Noun” and “noun as object” are arranged, and “adjective” relating to the noun is arranged on the left side. Also, an “adverb” that modifies the verb is placed on the right side of the “verb”. At this time, the information regarding the arrangement may be specified by using a setting file so that the user arranges “adjective”, “noun”, “verb”, and “adverb” in order from the left. Alternatively, a method may be used in which categories are arranged from left to right in descending order of the sum of the frequencies of words to be related. The above is the description of the operation of the dependency relationship structure configuration unit 109.

  Next, in step S207, the display unit 110 performs a visual display of the dependency relationship structure as illustrated in FIG. 8 based on the information generated by the dependency relationship structure configuration unit 109. For example, 801 “adjective”, 802 “noun: subject”, 803 “noun: object”, 804 “verb”, and 805 “adverb” in FIG. 8 represent categories. Further, reference numerals 806, 807, 808, 809, and 810 in FIG. 8 indicate lists of words belonging to categories corresponding to 801 to 805, respectively. At this time, the link information 811 has a direct dependency relationship between the word “difficult” having the “adjective” category and “display” having the “noun: subject” category, and the dependency frequency is high. Since the value given to the link acquired in step 703 in FIG. 7 is large, it is displayed in bold.

  As described above, according to the present invention, as shown in the display example of FIG. 8, the dependency relationship between words extracted from the text to be analyzed can be overviewed from the viewpoint of a category, and between individual words. It becomes possible to easily grasp the dependency relationship.

In addition, since each word shown in FIG. 8 is accompanied by the frequency counted by the category-specific word frequency totaling unit 108, the display target data operation unit 104 uses the categories 806 to 810 illustrated in FIG. For each of the display words, it is possible to specify to display from top to bottom in order of frequency, or to set the number of words to be displayed to only the specified number of high-frequency items. Although not explicitly shown in FIG. 8, the frequency of each word can be displayed together with the word.

In addition, the display target data operation unit 104 can display only the links to be displayed with a frequency equal to or higher than a certain threshold according to the dependency frequency. By changing the setting of the threshold value, it is possible to smoothly browse from the outline display to the detail display.

Further, in the display target data operation unit 104, the specified node (a pair of the category name 801 and the word group 806 belonging to the category in FIG. 8) is specified by the dependency relation regarding the word belonging to the category in the specified node. It is possible to specify whether to display or hide the node destination or source.

  In the display target data operation unit 104, a work box is newly inserted as a node on the display screen and can be displayed. At this time, by automatically moving a low frequency word whose frequency is equal to or less than a threshold value as a non-important word to a work box in the display screen or by moving a designated non-important word to a work box in the display screen, It becomes possible to reconstruct the dependency network structure centered on important words. Here, a non-important word may be automatically obtained by a statistical index that is generally used, such as a chi-square value or tf * idf value.

  In addition, the dependency relationship structure configuration unit 109 can define a similarity and group similar words as one concept. The similarity in this case is as follows: (1) Matching ratio of common partial character strings and editing distance (procedures necessary for transforming one character string into another character string by inserting, deleting, or replacing characters) (2) Similarity using a manually defined synonym dictionary or synonym dictionary, (3) Similarity based on vector distance of word appearance frequency in analysis target data, etc. be able to. A group grouped using such a similarity degree is composed of a set of similar words. Therefore, by using the group configured here as a display unit of a node displayed on the display unit 110, , Similar contents can be displayed together in one place.

  As described above, the text mining device according to the present invention includes the dependency relationship structure configuration unit 109 and the display target data operation unit 104, and is a conventional two-dimensional mapping method of dependency information focusing on the relationship between two words. This makes it possible to display a network in which dependency relationships between words are classified into three or more categories, which is difficult to do, and allows a broad overview.

  In addition, the display target data operation unit 104 can specify the number of display words, the order, and the like for the display word group in the category, so that the contents of the word set constituting each category can be easily grasped for each category. This makes it easier to find words that should be noted in the analysis.

  In the display target data operation unit 104, a link to be displayed by setting a threshold for the strength of relevance based on the dependency frequency or the like in a dependency relationship unit focusing on a relationship of three words or more. By controlling the number, it is easy to view from a summary display focused on highly relevant and important links to a detailed display that increases the number of links to be displayed and displays the relationship comprehensively, changing the granularity of the analysis. become able to.

  In addition, the display target data operation unit 104 can specify display / non-display or rearrangement of the dependency and the relation source link for the specified node, thereby narrowing down to the nodes and links to be noted. There is an advantage that it can be viewed.

  Further, by filtering the infrequent words or non-important words in the display target data operation unit 104, it is possible to reconstruct the dependency network structure centered on the important words.

  In addition, by grouping similar words as one concept in the dependency relationship structure configuration unit 109, it is possible to display similar contents together in one place and to easily understand the contents. .

  As described above, according to the present invention, a dependency relationship between a category and words belonging to the category is displayed on the network, and display information on the category, node, and link can be freely adjusted for the displayed network. By making it possible, it becomes possible to perform interactive analysis from the overall overview of the dependency relationship to the detailed refinement display.

Embodiment 2. FIG.
In the first embodiment described above, the dependency relationship between a category and words belonging to the category is displayed on the network, and the display information of the displayed network can be freely adjusted. Embodiment 2 in which a dependency relationship and a peripheral dependency relationship are extracted will be described.

  FIG. 9 is a block diagram showing Embodiment 2 of the present invention. In this configuration, in addition to the configuration of the first embodiment, the dependency relationship structure configuration unit 109 includes a core dependency relationship extraction unit 901 that extracts a dependency relationship whose dependency frequency is equal to or higher than a threshold, and a core dependency relationship. Each word constituting the core dependency relationship extracted by the relationship extraction unit 901 and a peripheral dependency relationship extraction unit 902 that extracts a dependency relationship having a frequency equal to or higher than a threshold value. Since other configurations are the same as those of the first embodiment, description thereof is omitted.

In the core dependency relationship extraction unit 901, in the dependency relationship network described in the first embodiment, the number of incoming links (number of dependency destinations) of a single word is equal to or greater than a threshold value, and the direct relationship of the corresponding word. A dependency relationship in which the dependency pair frequency in the reception relationship is greater than or equal to another threshold is extracted.

Next, the peripheral dependency relationship extraction unit 902 extracts a dependency relationship whose dependency relationship with each word constituting the core dependency relationship is a predetermined threshold or more. The display unit 110 displays the same dependency relationship network as in the first embodiment, focusing on the dependency relationship extracted by the core dependency relationship extraction unit 901 and the peripheral dependency relationship extraction unit 902.

FIG. 10 is an example of a display of the dependency relationship network in the second embodiment. In FIG. 2, the “guardian” (of the “guard” (of “the attitude (ni)-come to the head”, “the clerk (no) —attitude”, “unkind (na) —attitude)) ) -Attitude (ga) -bad "," confusing-attitude (ga) -coming to the head "surrounding dependency relationships can be extracted.

FIG. 11 is an example of an index used in the dependency relationship structure configuration unit 109 according to the second embodiment. In FIG. 11, 1101 is a word ID uniquely given to each word, 1102 is a list of record IDs (registration units extracted from the analysis target) ID, and 1103 is a sentence given to a sentence extracted from the record An ID list 1104 is a list of dependency IDs extracted from the record. At this time, it is assumed that the record ID, sentence ID, and dependency ID are in a hierarchical relationship. That is, in the dependency ID shown in FIG. 11 and expressed as ABC, A is a record ID, B is a sentence ID of record A, and C is a dependency ID of sentence B in record A. .

In general, if an attempt is made to maintain a dependency relationship between three words in an index, a huge amount of data is required. By configuring the dependency relationship index as shown in FIG. 11, the dependency relationship structure unit 109 extracts a dependency relationship of two words and selects only dependency relationships having a common sentence ID. Thus, only the pseudo three-word dependency relationship can be extracted. As a result, only the links having a pseudo three-word dependency relationship with strong connections can be selected and displayed on the display unit 110. At this time, “Unfriendly display” except when there is a dependency relationship between three words “Unfriendly attitude comes to mind” (“Unfriendly-Attitude-Head” is all a direct dependency relationship) If there is, there will be cases such as “I come to the head rather than the attitude”, but in the visualization for grasping the core dependency relationship, there is no problem regarding the purpose of the general overview.

  In addition, the display target data operation unit 104 allows the user to designate an arbitrary set of dependency groups as a composite concept. Next, another compound concept having a dependency relationship with a word belonging to the specified compound concept is retrieved and displayed in the index shown in FIG. For example, when “bad-mood-wait” is designated as a composite concept, other composite concepts such as “bad-mood-miss” and “wrong-registry-wait” are retrieved and displayed. As a result, it is possible to extract and display the relationship between complex concepts, and to analyze important relationship information that is latent in the analysis target.

  In the prior art, it is difficult to grasp the detailed contents around the high-frequency dependency relationship in the display content of the dependency relationship, whereas in the present invention, the dependency relationship structure configuration unit 109 is described above. With this configuration, the dependency relationships between words extracted from the text to be analyzed can be classified into three or more categories and displayed on the network, and in addition, the relationship between three or more words can be easily grasped. In addition, it is possible to analyze the details of the dependency relationships around the high-frequency dependency relationships.

  Further, in the present invention, by configuring the dependency relationship structure configuration unit 109 as described above, it is possible to display the dependency relationship between words extracted from the analysis target text by focusing on the dependency relationship of three or more words. Therefore, it is possible to easily grasp the contents by displaying a group of dependency relationships.

  Further, in the present invention, by configuring the display target data operation unit 104 as described above, a new relationship between composite concepts is extracted from a set of dependency relationships arbitrarily designated as a composite concept, and the analysis target It is possible to analyze important relationship information latent in

Embodiment 3 FIG.
In the second embodiment described above, it is possible to classify the dependency relationships between words into three or more categories and perform network display using the index structure, and to display a high-frequency dependency relationship. The configuration is such that it is easy to grasp the contents of detailed dependency relationships in the vicinity. Next, the semantic category dictionary 115 depends on the field to be analyzed with the same configuration as in the first embodiment. Embodiment 3 will be described in which the semantic categories stored are stored to facilitate the understanding and analysis of the contents of the field-dependent dependency relationship.

  In the category-specific word frequency totaling unit 108 according to the first embodiment, the semantic categories described in the semantic category dictionary 115 are “adjective”, “noun: subject”, “noun: object”, “verb”, and the like. An example of tabulation based on parts of speech in general language analysis was shown. In the third embodiment, correspondence information between semantic categories and words depending on the field is stored in the semantic category dictionary 115. Accordingly, the dependency network structure can be displayed on the display unit 110 for each field-dependent semantic category.

  The configuration diagram showing the third embodiment of the invention is the same as FIG. 1 used in the first embodiment. FIG. 12 is an example of the dependency relationship network display according to the third embodiment of the present invention. In this example, for the purpose of analyzing a failure report of a certain machine product, the “category name” of the target machine product, the “device attribute” related to the device name, and “ It is assumed that “phenomenon”, “condition” in which the phenomenon occurred, and “action” taken for the failure are stored as semantic categories.

  The words having the semantic category “device name” 1202 include “Z unit” and “main control device”, and the words having the semantic category “device attribute” 1201 include “group A” and “primary side”. Are stored as semantic categories. Similarly, “slip”, “display”, and the like are stored as words having the semantic category “phenomenon” 1203, and “inspection”, “exchange”, etc. are stored as words having the semantic category “action” 1204. It is assumed that “rainy weather”, “running”, and the like are stored as words having the semantic category “condition” 1205.

  Since the words as described above are stored in the semantic category dictionary 115, the display unit 110 depends on the semantic categories such as “device name” and “phenomenon” depending on the field as shown in FIG. The related network can be displayed.

  In the third embodiment, the word frequency totaling unit by category 108 totals the word frequencies based on the categories stored in the semantic category dictionary 115, and the display unit 110 stores the categories for each category stored in the semantic category dictionary 115. Although an example in which the dependency relationship network is displayed has been shown, it may be configured such that aggregation is performed for each attribute stored in the attribute DB 112 and the dependency network structure is displayed for each attribute in the display unit 110.

  By displaying the dependency network structure for each semantic category in the display means 110, the dependency relationship depending on the field to be analyzed can be grasped, and the analysis of the contents described in the document data can be facilitated. become able to.

  In addition, the display object data operation unit 104 can interactively edit the category to which each word belongs by referring to the dependency relationship between the words displayed on the display unit 110, and the name of the semantic category can be analyzed. The display can be changed according to the data, and the contents of the dependency relationship extracted from the target data can be easily understood.

It is a basic composition figure in Embodiment 1 of the present invention. It is a flowchart of the basic process in Embodiment 1 of this invention. It is a figure of the example of word DB in Embodiment 1 of this invention. It is a figure of the example of attribute DB in Embodiment 1 of this invention. It is a figure of the example of the dependency relation in Embodiment 1 of this invention. It is a figure of the example of dependency DB in Embodiment 1 of this invention. It is a flowchart of the dependency relationship structure configuration processing in the first embodiment of the present invention. It is a figure of the example of the dependency relation network display in Embodiment 1 of this invention. It is a block diagram in Embodiment 2 of this invention. It is a figure of the example of the dependency relation network display in Embodiment 2 of this invention. It is a figure of the example of the index of the dependency relation structure structure part in Embodiment 2 of this invention. It is a figure of the example of the dependency relation network display in Embodiment 3 of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 101 Registration part, 102 Dependency extraction part, 103 Input part, 104 Display object data operation part, 105 Control part, 106 Attribute value frequency totaling part, 107 Dependency frequency totaling part, 108 Word frequency totaling part according to category, 109 Dependency Relationship structure component, 110 display, 111 analysis object, 112 attribute DB, 113 word DB, 114 dependency DB, 115 semantic category dictionary, 116 visualization display of dependency relationship structure, 801 category “adjective”, 802 category “ Noun: Subject, 803 Category “Noun: Object”, 804 Category “Verb”, 805 Category “Adverb”, 806-810 List of words belonging to category, 811 Link information, 901 Peripheral dependency relationship extraction unit, 902 Core Target dependency relationship extraction unit, 1101 given to each word uniquely List of word IDs, 1102 IDs of records in which words appear (registration units extracted from the analysis target) IDs, lists of sentence IDs assigned to sentences extracted from 1103 records, lists of dependency IDs extracted from 1104 records, 1201 Semantic category “device attribute”, 1202 semantic category “device name”, 1203 semantic category “phenomenon”, 1204 semantic category “action”, 1205 semantic category “condition”.

Claims (14)

Registering means for analyzing the analysis target data and registering the word information extracted from the text of the analysis target data and the attribute value of the analysis target data;
A dependency extraction means for analyzing the extracted word information and extracting a dependency relationship between two or more words;
Dependency frequency totaling means for totalizing the frequency of dependency relationships between the words;
Attribute value frequency counting means for counting the frequency of the attribute value;
Category-based word totalizing means for totaling the word information extracted by the registration means and totalized for the attribute value frequency by the attribute value frequency totaling means for each predefined category;
Classifying the words constituting the dependency relationship between the words by category, and configuring the dependency relationship structure configuring means for configuring the dependency relationship network structure between the words included in the classified category;
Display means for displaying the dependency relationship network structure between the words;
A text mining device comprising: The display condition set includes: input means for receiving an input of setting of display conditions; and display target data operating means for instructing setting of the display conditions for the displayed dependency relation network structure between the words. The text mining device according to claim 1, wherein the display unit displays a dependency relationship network structure between the words.   The text mining device according to claim 2, wherein the display target data operation means designates the number of display words and the display order for the display word group in the category. The dependency relationship network between the words is limited by limiting the links to be displayed by setting the threshold of the relationship strength based on the frequency of the dependency relationship or the conceptual similarity by the display object data operating means. 3. The text mining apparatus according to claim 2, wherein the text mining apparatus displays from a schematic display to a detailed display of the structure.   3. The display target data operation means designates a word in the category as a node, and designates display, non-display, or rearrangement regarding a dependency and a link of a dependency source for the specified node. The text mining device described in 1.   By the display object data operating means, non-important words and low-frequency words are moved from the dependency relationship network structure between the words to a work box in the display screen, and the dependency network structure between words centered on the important words is obtained. The text mining device according to claim 2, wherein the text mining device is reconstructed.   7. The text mining device according to claim 1, wherein the dependency relationship structure constituting unit groups similar words as one concept for the node.   A core dependency relationship extracting unit that extracts a dependency relationship having a frequency of a predetermined number or more by the dependency relationship structure forming unit, and each of the dependency relationships extracted by the core dependency relationship extracting unit. The text mining device according to claim 1, further comprising a peripheral dependency relationship extracting unit that extracts a dependency relationship having a frequency more than a certain number of words.   9. The text mining device according to claim 8, wherein the peripheral dependency relationship extracting unit extracts only links having a three-word dependency relationship that is a dependency relationship between three words.   By designating an arbitrary set of dependency groups as a composite concept by the display target data operation means, search for another composite concept co-occurring with the specified composite concept, and a new co-occurrence relationship between the composite concepts The text mining apparatus according to claim 2, wherein the important co-occurrence relation latent in the analysis target data is displayed. In the category-specific word frequency counting means, counting is performed for each semantic category described in a semantic category dictionary, and the display means displays a dependency network structure of each word between the categories for each semantic category. The text mining device according to claim 1.   3. The category to which each word belongs can be interactively edited by the display target data operating means with reference to a dependency network structure of each word between the categories displayed on the display means. The text mining device according to any one of 11 to 11. A registration step of registering word information extracted from the text of the analysis target data and attribute values of the analysis target data by analyzing the analysis target data;
A dependency extraction step of analyzing the extracted word information and extracting a dependency relationship between two or more words;
Aggregating the frequency of the dependency relationship between the words, summing up the frequency of the attribute value,
A word summarizing step by category for summarizing the word information extracted by the registration step and summed up the frequency of the attribute value for each predefined category;
Classifying the words constituting the dependency relationship between the words by category, and configuring a dependency relationship network structure between the words included in the classified category;
A display step for displaying a dependency relationship network structure between the words;
An input step for receiving display condition setting input;
The dependency relationship network structure between the words is displayed according to the display condition set by the display target data operation step for instructing setting of the display condition for the displayed dependency relationship network structure between the words. Display step,
Text mining program to function as. Analyzing the analysis target data, registering the word information extracted from the text of the analysis target data and the attribute value of the analysis target data,
Analyzing the extracted word information to extract a dependency relationship between two or more words,
Aggregate the frequency of dependency relationships between the words and the frequency of the attribute values;
After the word information extracted by the registration and the frequency of attribute values is totalized for each predefined category,
Classifying the words constituting the dependency relationship between the words by the category, configuring a dependency relationship network structure between the words included in the classified category,
Display the dependency relationship network structure between the words,
Accept input of display condition setting,
A text mining method comprising instructing setting of the display condition to the displayed dependency relation network structure between words and displaying the dependency relation network structure between words according to the display condition.

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