Source: https://patents.google.com/patent/JP5023176B2/en
Timestamp: 2020-03-31 10:46:48
Document Index: 382865274

Matched Legal Cases: ['art 54', 'art, 20', 'art, 30', 'art, 40', 'art, 41', 'art, 42', 'art, 43', 'art 44']

JP5023176B2 - Feature word extraction apparatus and program - Google Patents
Feature word extraction apparatus and program Download PDF
JP5023176B2
JP5023176B2 JP2010064821A JP2010064821A JP5023176B2 JP 5023176 B2 JP5023176 B2 JP 5023176B2 JP 2010064821 A JP2010064821 A JP 2010064821A JP 2010064821 A JP2010064821 A JP 2010064821A JP 5023176 B2 JP5023176 B2 JP 5023176B2
JP2010064821A
JP2011198111A (en
泰成 宮部
秀樹 岩崎
2010-03-19 Application filed by 東芝ソリューション株式会社, 株式会社東芝 filed Critical 東芝ソリューション株式会社
2010-03-19 Priority to JP2010064821A priority Critical patent/JP5023176B2/en
2011-10-06 Publication of JP2011198111A publication Critical patent/JP2011198111A/en
2012-09-12 Publication of JP5023176B2 publication Critical patent/JP5023176B2/en
The present invention relates to a feature word extraction apparatus and program for extracting feature words, which are words characterizing the contents of each document, from a large number of digitized documents.
In patent research and questionnaire analysis, there is a need to extract feature words and compare the contents and trends of multiple document sets. For example, in patent research, there is a need to compare the tendency of patent applications of each age between the company and competitors. The quality of this type of survey is influenced by the scope of comparison and feature terms selected.
However, appropriate comparison ranges and feature words differ depending on the purpose of the survey and the contents of the document set. For this reason, selection of comparison ranges and feature words requires knowledge related to the contents of the document set and skills related to the survey itself based on the purpose.
On the other hand, Patent Document 1 proposes a technique for presenting an appropriate analysis axis. The technique described in Patent Literature 1 extracts words for each attribute included in the data, totals the appearance frequencies for each attribute value of the words by a totaling unit, and displays the totaled appearance frequencies for the user so that it can be easily seen. At the same time, attributes suitable for analysis are extracted from the appearance tendency of words and presented to the user. This assists the user in selecting an appropriate classification axis.
In addition, in the technique described in Patent Document 2, in order to select an appropriate feature word to be used for analysis, a feature that is extracted for each attribute value (for example, every month) for an attribute to be compared (for example, a creation date) Based on word co-occurrence relationships, differences are presented in each attribute. As a result, the contents of the document set can be analyzed more suitably. Note that “co-occurrence” used in the technique described in Patent Document 2 is summarized in Patent Document 3, for example.
JP 2006-171931 A Japanese Patent Laid-Open No. 2002-245070
Masao Uchiyama, Kiyomi Nakajo, Hideko Yamamoto, Hitoshi Isahara. "Comparison of selection scales of field feature words for English education", Natural Language Processing, 11 (3), 165-197, 2004. Kishida Kazuaki. "The nature of average accuracy as an evaluation index in search experiments", IPSJ Transactions: Database, Vol. 43, SIG2 (TOD13) (2002). Akiko Aizawa. "Similarity measure based on co-occurrence", Operations Research, November 2007, pp.706 (20) -712 (26).
However, the techniques described in Patent Documents 1 and 2 as described above usually have no particular problem, but according to the study of the present inventor, there is room for improvement in the following points.
For example, in the technique described in Patent Document 1, an analysis axis presented to the user needs to be defined in advance as an attribute of document data. Therefore, since the analysis axis to be presented is limited to the predefined attributes, there is room for improvement in that the intended analysis cannot be performed.
The technique described in Patent Literature 2 attempts to clarify the contents of a document set by representing differences with respect to each attribute value. For this reason, there is room for improvement in that the object of analysis is bound to attribute values, and the user cannot compare document sets within an arbitrary range.
Further, the technique described in Patent Document 2 may not be able to grasp what the user should focus on in a document set when comparatively analyzing a huge document set. For example, consider a case where a cross-analysis of several thousand and tens of thousands of patent documents is performed with the applicant and the date of application (each month from 1990 to 2008) in order to conduct a prior investigation of the “image recognition” technique of interest. When about 10 to 100 companies are arranged in each row as applicants, and about 100 months are arranged in each column as the filing date, about 10,000 cells are formed as a whole cross analysis matrix. The
These cells include cells that contain many patent documents related to “image recognition” and cells that contain many patent documents that are not related to “image recognition”. The degree is uneven. The same can be said for each company and application year.
On the other hand, there is a need for the user to conduct a more precise investigation by narrowing down the comparison range to a cell document set related to a company or application year that is strongly related to the technology of interest.
However, the techniques described in Patent Documents 1 and 2 cannot support narrowing down of the comparison range to be noted, and the comparison range cannot be flexibly changed. In addition, by referring to feature words of cells that are strongly related to the technology of interest, the user can discover related technologies that he / she was unaware of, but with the technologies described in Patent Documents 1 and 2, the content of the document set is limited to understanding, It is not possible to support the reference of feature words to be newly focused.
The present invention has been made in consideration of the above circumstances, and can present feature words as candidates for analysis axes without being limited to attributes defined in advance, narrow down the comparison range to be noticed, and feature words to be noticed. It is an object of the present invention to provide a feature word extraction device and a program that can support the reference of.
One aspect of the present invention is a feature word extraction device that stores a plurality of documents having document IDs and content text information in association with one or more document IDs for each category ID. A category storage unit, a document feature word storage unit that stores, for each document in the document storage unit, a document ID of the document and a document feature word extracted from the content text information of the document in association with each other; and the category Category feature word storage means for storing the category ID and one or more document IDs related in the storage means in association with the category feature words related to the category ID, and content text for each document in the document storage means Morphological analysis of the information, extracting the document feature word from the result of the morpheme analysis, the extracted document feature word, and the document ID of the document corresponding to the document feature word For each document feature word in the document storage means, the number of documents in which the document feature word appears is calculated for each document feature word in the document storage means. For each category ID in the category storage unit, the document feature word related to the document ID associated with the category ID appears in the document with the document ID. Based on the calculated number of documents appearing in the category document, the number of documents calculated by the number of appearing documents in all documents, and the number of documents calculated by the number of appearing documents in category documents, Feature calculation means for calculating the feature of the document feature word for the document related to the category ID in the document, and category feature with the feature added to the document feature word Category feature word creating means for associating the created category feature word with a category ID and one or more document IDs related to the category feature word, and writing the category feature word into the category feature word storage means, and the category storage means Category number presentation means for presenting category number data including the number of document IDs associated with the category ID, and selection of any category number data during the presentation of the category number data. Among the category feature words related to the category ID of the category number data selected by the category number data selection receiving means and the category number data received by the category number data selection receiving means, the document feature words in the category feature words having the higher feature values are classified as category features. Category feature word presenting means for presenting as words, and the category A comparison target set selection accepting means for accepting selection of a comparison target set consisting of a plurality of comparison targets that is a union of the category IDs of the plurality of category number data during the presentation of the category number data by the re-character word presenting means; For each document feature word related to each document ID associated with each category ID in the comparison target set that has been selected by the comparison target set selection receiving means, the document feature in all documents related to the document ID A first document number calculation unit that calculates the number of documents in which a word appears and a comparison target in the comparison target set that has received selection by the comparison target set selection reception unit are associated with each category ID in the comparison target. A second document number calculation means for calculating the number of documents in which the document feature word related to each document ID appears in the document of each document ID, and the comparison target set selection The document number calculated by the first document number calculating unit and the second document for each document feature word related to each document ID associated with each category ID in the comparison target set that has been selected by the attaching unit Based on the number of documents calculated by the number calculation means, a difference feature degree of a degree representing the difference of the document feature word in each comparison target is calculated, and a document feature word having a higher difference feature degree is classified as a category difference feature word A category difference feature word sending means for sending as a category difference feature word presenting means for presenting a category difference feature word sent by the category difference feature word sending means.
Although one aspect of the present invention is expressed as an apparatus, the present invention is not limited thereto, and may be expressed as a method, a program, or a computer-readable storage medium storing a program.
In one aspect of the present invention, among the category feature words related to the category ID of the category number data that has been selected during the presentation of the category number data including the number of document IDs related to the category ID, A document feature word in a category feature word having a higher feature degree is presented as a category feature word.
Further, in one aspect of the present invention, during the presentation of the category number data, when selection of a comparison target set consisting of a plurality of comparison targets that is a union of the category IDs of the plurality of category number data is received, the selection is performed. For each document feature word related to each document ID associated with each category ID in the received comparison target set, a difference feature degree of a degree representing the difference of the document feature word in each comparison target is calculated, and the difference feature Document feature words with higher degrees are presented as category difference feature words.
As described above, the feature word such as the category feature word or the category difference feature word is presented according to the selected category number data, so that the feature word can be presented as a candidate for the analysis axis without being limited to the predefined attribute. At the same time, it is possible to assist in narrowing down the comparison range to be noticed and referring to feature words to be noticed.
As described above, according to the present invention, it is possible to present feature words as analysis axis candidates without being limited to attributes defined in advance, and to narrow down a comparison range to be noticed and to refer to feature words to be noticed. Can support.
It is a block diagram which shows the structure of the feature word extraction apparatus which concerns on one Embodiment of this invention. It is a schematic diagram for demonstrating the document memory | storage part in the same embodiment. It is a schematic diagram for demonstrating the category memory | storage part in the same embodiment. It is a schematic diagram for demonstrating the feature word memory | storage part in the embodiment. It is a flowchart for demonstrating operation | movement of the feature word extraction part in the embodiment. It is a flowchart for demonstrating operation | movement of the category feature word extraction part in the same embodiment. It is a flowchart for demonstrating operation | movement of the category common feature word extraction part in the embodiment. It is a schematic diagram which shows the example of a screen which showed the category number data in the embodiment to the cell. It is a schematic diagram which shows the example of a screen which selected the comparison object set in the embodiment. It is a schematic diagram which shows the example of a screen which displayed the common feature word in the embodiment. It is a flowchart for demonstrating operation | movement of the category difference characteristic word extraction part in the same embodiment. It is a schematic diagram which shows the example of a screen which displayed the common feature word and the different feature word in the embodiment. It is a flowchart for demonstrating operation | movement of the related category presentation part in the embodiment. It is a flowchart for demonstrating operation | movement of the user operation and presentation part in the embodiment. It is a schematic diagram which shows the example of a screen which displayed the category characteristic word in the same embodiment. It is a schematic diagram which shows the example of a screen which highlighted the cell of the related category in the embodiment. It is a schematic diagram which shows the example of a screen when the attention word is selected from the category characteristic word in the embodiment. It is a schematic diagram which shows the example of narrowing down of the comparison object and the display of a feature word in the same embodiment. It is a schematic diagram which shows the example of a change of the attention word in the same embodiment, and the display of a related category. It is a schematic diagram which shows the example of a screen of the cross analysis in the same embodiment. It is a schematic diagram which shows the example of a screen of the other cross analysis in the same embodiment. It is a schematic diagram which shows the example of a screen of the other cross analysis in the same embodiment. It is a schematic diagram which shows the example of a screen of the graph display in the embodiment.
Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Note that the following devices can be implemented for each device in either a hardware configuration or a combination configuration of hardware resources and software. As the software of the combined configuration, a program that is installed in advance on a computer of a corresponding device from a network or a storage medium and that realizes the function of the corresponding device is used. The definitions of terms and symbols used in the following description are as shown in Tables 1 and 2 below.
In the embodiment described below, a document set including a plurality of documents is input, and the document feature word related to the document ID included in the document set is the number of documents appearing in the document with the document ID ( It can be said that the processing (concept including the number after the feature that appears in the document) (the function for calculating the number of appearing documents) has a particular feature.
Accordingly, the function for calculating the number of appearing documents will be described by taking the function for calculating the number of appearing documents in all documents, the function for calculating the number of appearing documents in the category document, the function for calculating the first document number, and the function for calculating the second document number as examples. To go. This is because, even if the types of input are different for all document sets, categories, comparison target sets (comparison target sets), comparison targets (category set) defined in each table above, This is because both can be referred to as a document set (a set of document IDs).
FIG. 1 is a block diagram showing a configuration of a feature word extraction apparatus according to an embodiment of the present invention, and FIGS. 2 to 4 are schematic diagrams for explaining the storage units 10, 20, and 30 in the apparatus. is there. The feature word extraction apparatus includes a document storage unit 10, a category storage unit 20, a feature word storage unit 30, a feature word extraction unit 40, and a user operation / presentation unit 50.
The document storage unit 10 is a storage device that can be read / written from each of the units 40 and 50, and stores document data 10d as shown in FIG. The document data 10d is data obtained by digitizing a plurality of documents having a document 11d as a document ID for identifying each document and a document name 12d and / or a text 15d as content text information (character string information) 12d. Here, examples of patent documents are shown. The document data 10d may have attribute values such as the application date 13d and the applicant 14d in addition to the document ID 11d and the content text information.
The category storage unit 20 is a storage device that can be read / written from the respective units 40 and 50, and stores associated document information 22c including one or more document IDs in association with each category ID 21c as shown in FIG. ing. Here, a set of the category ID and the belonging document information 22c is referred to as category data 20c. A set of document IDs in the affiliated document information 22c associated with one category ID 21c indicates a document set that is a minimum unit of feature word extraction, and this document set is also called a category. For example, a category identified by category ID = C01 belongs to a document identified by document ID = D17, D23, D41. This category data 20c is given in advance. For example, a classification result obtained by a document classification technique such as document clustering may be used as the category data 20c, and a set of documents divided by attribute values such as a document creation year and a creator may be used as the category data 20c. Further, it may be a category structure in which one document belongs to only one category, or a category structure in which one document belongs to a plurality of categories. The category data 20c may have attribute information such as a category name and a label in addition to the category ID 21c and the belonging document information 22c.
The feature word storage unit 30 is a storage device that can be read / written from each unit 40, 50, and stores document feature word data 30dt and category feature word data 30ct as shown in FIG.
The document feature word data 30dt is data in which for each document in the document storage unit 10, the document ID 31dt of the document is associated with the document feature word 32dt extracted from the content text information of the document. This document feature word 32dt is extracted by removing unnecessary words from the set of words obtained by morphological analysis of the content text information of the document data stored in the document storage unit 10 in the document feature word extraction unit 41. A set of words. Unnecessary words are removed by using part-of-speech such as nouns and unknown words, words that do not meet the conditions of the words used as feature words, and words that are highly general as “to” and “things” and inappropriate as feature words. . On the other hand, words with extremely low appearance frequency that appear only once in the document may be excluded as unnecessary words. The type of part-of-speech held can be changed according to the type of feature word extraction target such as a patent document or a mail document, or the purpose of feature word extraction such as survey or analysis. In this example, the document feature word 32dt is held as a word only as the document feature word data 30dt, but the word appearance frequency TF in the document may be held in association with the word of each document feature word 32dt. . The TF can be used as one index when obtaining a feature word of a word in feature word extraction.
The category feature word data 30ct is data in which the category ID 31ct and the affiliated document information 32ct that are the same as the category ID 21c and the document affiliation information 22c in the category storage unit 20 are associated with the category feature word 33ct related to the category ID 31ct. The category feature word 33ct includes each word that is the document feature word 32dt related to the document ID in the affiliated document information 32ct, and the feature added to each word.
The feature word extraction unit 40 includes a document feature word extraction unit 41, a category feature word extraction unit 42, a category common feature word extraction unit 43, and a category different feature word extraction unit 44. Note that the category common feature word extraction unit 43 and the category difference feature word extraction unit 44 can analyze the document set if one of them is present, so that either one can be left and the other can be omitted.
The document feature word extraction unit 41 performs morphological analysis on the content text information for each document in the document storage unit 10, extracts a document feature word from the result of morpheme analysis, and extracts the extracted document feature word and the document feature word. The document feature word data 30 dt associated with the document ID of the corresponding document is written in the document feature word storage unit 30. Here, the extraction of the document feature word may be performed by a process of eliminating unnecessary words (unnecessary words) in the feature word extraction, such as appearing only once in the document from the result of the morphological analysis.
The category feature word extraction unit 42 has the following functions (f42-1) to (f42-5).
(f42-1) A function for calculating the number of appearing documents in all documents, for each document feature word in the document storage unit 10, for calculating the number of documents in which the document feature word appears in all the documents in the document storage unit 10.
(f42-2) For each category ID 21c in the category storage unit 20, the document feature word related to the document ID associated with the category ID 21c appears in the category document for calculating the number of documents in the document with the document ID. Document number calculation function.
(f42-3) Documents related to the category ID 21c in all documents based on the number of documents calculated by the function for calculating the number of appearance documents in all documents and the number of documents calculated by the function for calculating the number of appearance documents in category documents A feature calculation function for calculating the feature of the document feature word for The feature level of a document feature word is calculated based on statistical information of document feature words of documents belonging to a category.
(f42-4) A function of creating a category feature word 33ct in which the feature level is added to the document feature word.
(f42-5) A function of writing the category feature word data 30ct in which the created category feature word 33ct, the category ID 31ct related to the category feature word 33ct, and the belonging document information 32ct are associated with each other to the feature word storage unit 30.
The category common feature word extraction unit 43 has the following functions (f43-1) to (f43-3).
(f43-1) For each document feature word related to each document ID of the affiliated document information 22c associated with each category ID 21c in the comparison target set that the common / difference feature word presentation unit 53 has received selection by a user operation, A first document number calculation function for calculating the number of documents in which the document feature word appears in all documents related to each document ID.
(f43-2) For each comparison target in the comparison target set that has received the selection, a document feature word related to each document ID of the affiliated document information 22c associated with each category ID 21c in the comparison target is the corresponding document ID. A second document number calculation function for calculating the number of documents appearing in the document.
(f43-3) The number of documents calculated by the first document number calculation function for each document feature word related to each document ID of the belonging document information 22c associated with each category ID 21c in the comparison target set that has received the selection Based on the number of documents calculated by the second document number calculation function, the common feature degree of the degree representing the commonality of the document feature words in the comparison target set is calculated, and the document feature word having the higher common feature degree A category common feature word sending function that sends to the common / different feature word presentation unit 53 as a category common feature word. Here, the common feature degree is a degree representing the commonality of each feature word in each comparison target set, and is calculated based on statistical information of a document set of documents belonging to the comparison target set.
The category difference feature word extraction unit 44 has the following functions (f44-1) to (f44-3).
(f44-1) For each document feature word related to each document ID of the belonging document information 22c associated with each category ID 21c in the comparison target set that the common / difference feature word presentation unit 53 has received selection by a user operation, A first document number calculation function for calculating the number of documents in which the document feature word appears in all documents related to each document ID.
(f44-2) For each comparison target in the comparison target set that has received the selection, the document feature word related to each document ID of the affiliated document information 22c associated with each category ID 21c in the comparison target is the corresponding document ID. A second document number calculation function for calculating the number of documents appearing in the document.
(f44-3) The number of documents calculated by the first document number calculation function for each document feature word associated with each document ID of the affiliated document information 22c associated with each category ID 21c in the comparison target set that has received the selection And the number of documents calculated by the second number-of-documents calculation function, the difference feature degree of the degree representing the difference of the document feature word in each comparison target is calculated, and the document feature word having the higher difference feature degree A category difference feature word sending function for sending to the common / difference feature word presentation unit 53 as a category difference feature word. Here, the difference feature degree is a degree representing the difference of each feature word in each comparison target set, and is calculated based on the statistical information of the document set of documents belonging to each comparison target.
The user operation / presentation unit 50 includes a screen presentation unit 51, a category feature word presentation unit 52, a difference / common feature word presentation unit 53, and a related category presentation unit 54. The related category presentation unit 54 is not essential for the analysis of the document set, and may be omitted.
The screen presentation unit 51 has a function of creating screen data with reference to each of the storage units 10, 20, and 30 according to a user operation, and a function of presenting a screen based on the screen data. Here, as the screen data, for example, for each category ID 21c in the category storage unit 20, category number data including the number of document IDs in the document affiliation information 22c associated with the category ID 21c is presented in each cell. Analysis screen data, cross-analysis screen data presented to each cell after modifying the category count data so that the selected feature word is the analysis axis, and graph display based on the screen data of the cross-analysis screen being presented Screen data of a screen, or screen data of a cross analysis screen based on screen data of a graph display screen being presented. In addition to the cross display and the graph display, the display format may be a map display in which a document set is represented by an ellipse on a plane, or a folder display used in a computer file system.
The category feature word presentation unit 52 accepts selection of a category by the user, and acquires category feature word data corresponding to the selected category from the category feature word storage unit 30. Based on the acquired category feature word data, a feature word having a higher feature in the category is presented to the user as a category feature word.
The difference / common feature word presentation unit 53 receives the selection of the comparison target set by the user, sends the selected comparison target set to the feature word extraction unit 40, and the common feature word received from the feature word extraction unit 40 It has a function to present, and a function to present these different feature words in association with each comparison target when receiving the respective different feature words for each comparison target from the feature word extraction unit 40.
The related category presentation unit 54 accepts selection of the focused word set by the user, calculates the degree of association between the focused word set and each category based on the category feature word data stored in the feature word storage unit 30, It has a function of highlighting the corresponding category number data with a category having a large value as a related category. Here, the category having a high relevance level is a category having a relevance level equal to or higher than a threshold value.
Next, the operation of the feature word extraction apparatus configured as described above will be described with reference to the flowcharts and schematic diagrams of FIGS.
(Operation of the feature word extraction unit 41: FIG. 5)
In general, the feature word extraction unit 41 morphologically analyzes the content text information for each document in the document storage unit 10, extracts a document feature word from the result of the morpheme analysis, the extracted document feature word, A process of writing in the feature word storage unit 30 in association with the document ID of the document corresponding to the document feature word is executed (S1 to S4).
Specifically, the feature word extraction unit 41 acquires the entire document set docAll, which is a set of all document data to be analyzed, from the document storage unit 10 (S1).
Next, the feature word extraction unit 41 repeats the processes of step S3 and step S4 for each document data doc included in the entire document set docAll (S2).
That is, the feature word extraction unit 41 performs morphological analysis on the content text information for each document data doc (S3). Also, the feature word extraction unit 41 creates a word group extracted from the result of the morphological analysis by excluding words other than the part of speech that is the target of feature word extraction and unnecessary words such as “ko” and “thing”. Let it be a feature word. Thereafter, the feature word extraction unit 41 writes the document feature word data in which the extracted document feature word is associated with the document ID in the feature word storage unit 30 (S4).
For example, in the case of the document data shown in FIG. 2, when the body text 25 is the content text information to be analyzed, the document feature word data 30dt is converted into the feature word storage unit 30 as shown in FIG. Is written to.
(Operation of category feature word extraction unit 42: FIG. 6)
The category feature word extraction unit 42 roughly includes, for each document feature word in the document storage unit 10, the number of documents df (t, docAll) in which the document feature word appears in all the documents in the document storage unit 10. And the document feature word 32 dt related to the document ID of the affiliated document information 22 c associated with the category ID 21 c for each category ID 21 c in the category storage unit 20. The number of documents calculated by the number-of-category document number calculation process (S14 to S18) for calculating the number of documents df (t, cat) appearing in the document with the document ID and the number of documents calculated by the number-of-documents calculation process in all documents Based on df (t, docAll) and the document number df (t, cat) calculated by the number-of-appearance document number calculation process in the category document, the category ID in all documents Feature calculation processing (S19) for calculating the feature score (t, cat) of the document feature word 32dt for the document related to 1c, and the feature score (t, cat) are added to the document feature word 32dt A category feature word 33ct is created, and the created category feature word 33ct, category ID 31ct related to the category feature word (same value as the category ID 21c), and affiliated document information 32ct (affiliated document information 22c) including one or more document IDs And the same value) are written in the feature word storage unit 30 (S20).
Specifically, the category feature word extraction unit 42 acquires the document feature word data of all the documents docAll from the feature word storage unit 30 (S11).
Next, the category feature word extraction unit 42 repeats the process of step S13 for each document feature word t included in the document feature word data obtained in step S11 (S12).
That is, for each document feature word t, the category feature word extraction unit 42 refers to the document feature word data of all documents docAll, and the number of documents df (t in which the document feature word t appears in the entire document set docAll. , DocAll) (S13) is repeated.
Thereafter, the category feature word extraction unit 42 acquires all the category data 20c from the category storage unit 20 (S14).
Further, the category feature word extraction unit 42 repeats the processing of steps S16 to S20 for each category ID 21c for all categories cat (S15).
Further, the category feature word extraction unit 42 reads out the document feature word related to the document ID of the affiliated document information 22c associated with the category ID 21c from the feature word storage unit 30, so that the category cat word is extracted from the feature word storage unit 30. Document feature word data of each document is acquired for a plurality of documents belonging to (S16).
Subsequently, the category feature word extraction unit 42 repeats the process of step S18 for each document feature word t included in the acquired document feature word data (S17).
The category feature word extraction unit 42 refers to the document feature word data acquired in step S16 for each document feature word t, and the number of documents df in which the document feature word t appears in a plurality of documents belonging to the category cat. (T, cat) is obtained (S18).
The category feature word extraction unit 42, based on the document number df (t, docAll) obtained in step S13 and the document number df (t, cat) obtained in step S18, the feature word t for the category cat in the entire document set docAll. The feature score (t, cat) is calculated (S19).
Specifically, the feature score (t, cat) is based on the number of documents df (t, docAll), df (t, cat), as shown in Table 3, common parameters a, b, c, After d and n are calculated, they are calculated as any statistical index as shown in Table 4.
Here, the feature score score (t, cat) of each word t is calculated as a statistical index called log likelihood ratio LLR. However, the statistical index is not limited to the log likelihood ratio LLR, and may be, for example, a dice coefficient Dice, a Yates correction χ square value Yates ′, or a self mutual information amount MI. In addition, since each statistical index has a characteristic, the tendency of the characteristic word obtained differs according to the characteristic of each statistical index.
For example, the dice coefficient Dice highly evaluates a word t having a large number of documents df (t, cat) in which the word t appears in the category cat (words frequently included in the category cat (high frequency)).
The Yates correction chi-square value Yates' highly evaluates words having a high appearance probability in the category cat with respect to the appearance probability in the entire document set docAll. As a result, in the Yates correction χ-square value Yates ′, relatively low-frequency words are more likely to be extracted as feature words than when the log likelihood ratio LLR and the dice coefficient Dice are used.
The self-mutual information amount MI highly evaluates a word having a large bias between the appearance probability in the entire document set docAll and the appearance probability in the category cat. However, since the self-mutual information amount MI tends to overestimate low-frequency words, when used, it is necessary to perform processing such as excluding words with extremely small df (t, cat) from feature words. Details of each of the above statistics are described in Non-Patent Document 1.
The category feature word extraction unit 42 adds the feature score score (t, cat) of each feature word calculated in step S19 as category feature word information 33ct added to the feature word to the category data 20c of the category cat. The feature word data 30ct is stored in the feature word storage unit 30 (S20).
(Operation of category common feature word extraction unit 43: FIG. 7)
As shown in FIG. 8, the screen presentation unit 51 displays a screen G10 that presents category number data including the number of document IDs associated with the category ID 21c for each category ID 21c in the category storage unit 20. For example, the cells c1 and c2 in the screen G10 are displayed for each category ID 21 (not shown), and the values “75” and “50” in the cells c1 and c2 correspond to the category number data.
The difference / common feature word presenting unit 53 performs comparison of a plurality of comparison target cmp_i, which is a union of the category IDs of the plurality of category number data, by the user's operation during the presentation of the category number data by the screen presenting unit 51. Selection of the target set tgtSet is accepted. For example, in the case shown in FIG. 9, the first comparison target cmp1 is the category ID of each of the five category number data “65”, “50”, “69”, “75”, “72” surrounded by the solid line frame f1. The second comparison object cmp2 is the category ID of each of the five category number data “10”, “21”, “45”, “53”, “35” surrounded by the dotted line frame f2. It is a union.
The category common feature word extraction unit 43 is generally associated with each document ID of the affiliated document information 22c associated with each category ID 21c in the comparison target set tgtSet received by the difference / common feature word presentation unit 53. First document number calculation processing (S21 to S25) for calculating the number of documents df (t, tgtDocs) in which the document feature word appears in all the documents (tgtDocs) related to each document ID for each document feature word. ), And for each comparison target cmp_i in the comparison target set tgtSet that has received the selection, the document feature word related to each document ID of the affiliated document information 22c associated with each category ID 21c in the comparison target cmp_i Second document number calculation processing (S26 to S29) for calculating the number of documents df (t, cmp) appearing in the document of ID, and selection The document number df (t, tgtDocs) calculated by the first document number calculation process for each document feature word related to each document ID of the affiliated document information 22c associated with each category ID 21c in the comparison target set tgtSet. ) And the number of documents df (t, cmp) calculated by the second number-of-documents calculation process, the common feature degree com (t, tgtSet) indicating the commonality of the document feature words in the comparison target set tgtSet ) Is calculated, and a process of sending document feature words having higher common feature values com (t, tgtSet) to the different / common feature word presentation unit 53 as category common feature words is executed (S30 to S31).
Specifically, the category common feature word extraction unit 43 receives a comparison target set tgtSet including a plurality of comparison target cmp (each document ID) corresponding to each category number data selected by the user from the user operation / presentation unit 50. Is acquired (S21).
The category common feature word extraction unit 43 obtains a comparison range tgtDocs by taking the union of all comparison target cmp included in the comparison target set tgtSet (S22).
The category common feature word extraction unit 43 acquires document feature word data related to all document IDs included in the comparison range tgtDocs from the feature word storage unit 30 (S23).
The category common feature word extraction unit 43 repeats step S25 for all feature words t included in the document feature word data acquired in step S23 (S24).
The category common feature word extraction unit 43 refers to the document feature word data acquired in step S23, and the number of documents in which the feature word t appears in documents related to the document ID included in the comparison range tgtDocs df (t , TgtDocs) is obtained (S25).
The category common feature word extraction unit 43 repeats the processes of steps S27 to S29 for each comparison target cmp included in the comparison target set tgtSet (S26).
The category common feature word extraction unit 43 acquires document feature word data related to the document ID of the comparison target cmp from the feature word storage unit 30 (S27).
The category common feature word extraction unit 43 repeats the process of step S29 for all feature words t included in the document feature word data acquired in step S27 (S28).
The category common feature word extraction unit 43 refers to the document feature word data acquired in step S27, and the number of documents df (t, cmp) in which the word t appears in the documents related to the document ID of the comparison target cmp. Is obtained (S29).
The category common feature word extraction unit 43 includes the number of documents df (t, tgtDocs) in which words appear in the comparison range tgtDocs calculated in step S25 and the number of documents in which words appear in each comparison target cmp calculated in step S29. Based on df (t, cmp), the common feature com (t, tgtSet) in the comparison target set tgtSet is calculated for all the words t included in the document within the comparison range tgtDocs (S30).
Specifically, when calculating the common feature level com (t, tgtSet), first, based on the number of documents df (t, tgtDocs), df (t, cmp), as shown in Table 5, After calculating the parameters a ′, b ′, c ′, d ′, n ′, the evaluation value eval (t, cmp_i) is calculated as one of the statistical indexes as shown in Table 6.
Subsequently, the sum of the evaluation values eval (t, cmp_i) of the comparison object cmp_i is calculated, and the obtained sum is set as the common feature com (t, tgtSet) of the word t in the comparison range tgtDocs.
In this index, a word that is included as a feature word in more comparison target cmp_i and has a higher evaluation value eval (t, cmp_i) in each comparison target cmp_i is highly evaluated as a common feature word.
Here, for example, the common feature word com (t, tgtSet) of the word is obtained using a statistical index called log likelihood ratio LLR. Instead of the log likelihood ratio LLR, a statistical index such as the Yeats χ square value or the self-mutual information MI described above may be used.
In such a statistical index, the appearance frequency df (t, docAll) of each feature word t in the entire document set docAll and the appearance frequency df (t, cat) of each word in each category cat may be used.
After that, the category common feature word extraction unit 43 performs the user operation / presentation using the top r words as the common feature words comTerms of the tgtSet for the common feature degree com (t, tgtSet) of each feature word calculated in step S30. The data is sent to the unit 50 (S31).
Here, r is a set value of the number of feature words to be presented in the presentation of common feature words, different feature words, and category feature words, which may be set in advance and set each time feature word extraction is performed. Also good. Further, although the feature words having the common feature degree com (t, tgtSet) within the top r are set as the common feature words, the feature words having the common feature degree com (t, tgtSet) equal to or greater than the threshold are not limited thereto. It may be a common feature word.
The difference / common feature word presentation unit 53 presents the r category common feature words sent in step S31 in the list Lcom, as shown in FIG.
(Operation of Category Difference Feature Word Extraction Unit 44: FIG. 11)
As shown in FIG. 8, the screen presentation unit 51 displays a screen G10 that presents category number data including the number of document IDs associated with the category ID 21c for each category ID 21c in the category storage unit 20.
The difference / common feature word presenting unit 53 performs comparison of a plurality of comparison target cmp_i, which is a union of the category IDs of the plurality of category number data, by the user's operation during the presentation of the category number data by the screen presenting unit 51. Selection of the target set tgtSet is accepted.
The category difference feature word extraction unit 44 generally includes belonging document information associated with each category ID 21c in the comparison target set tgtSet received by the difference / common feature word presentation unit 53 as shown in FIG. For each document feature word related to each document ID of 22c, the first document for calculating the number of documents df (t, tgtDocs) in which the document feature word appears in all documents (tgtDocs) related to each document ID Number calculation processing (S41 to S45), and for each comparison target cmp_i in the comparison target set tgtSet that has received the selection, each document ID of the affiliated document information 22c associated with each category ID 21c in the comparison target cmp_i Second document number calculation processing (S46-) for calculating the number of documents df (t, cmp) in which the document feature word appears in the document of each document ID. 49) and the number of documents calculated by the first document number calculation process for each document feature word related to each document ID of the affiliated document information 22c associated with each category ID 21c in the comparison target set tgtSet that received the selection. Based on df (t, tgtDocs) and the number of documents df (t, cmp) calculated by the second document number calculation process, the difference feature degree indicating the difference between the document feature words in each comparison target cmp_i Diff (t, cmp) is calculated, and a process of sending a document feature word having a higher difference feature degree diff (t, cmp) as a category difference feature word to the difference / common feature word presentation unit 53 is executed (S50 to S51). ).
Specifically, the category different feature word extraction unit 44 executes Steps S41 to S49 in the same manner as Steps S21 to S29 described above. Instead of steps S41 to S49, the category difference feature word extraction unit 44 may be modified so that the results of steps S21 to S29 are used in step S50. Conversely, instead of steps S21 to S29, the category common feature word extraction unit 43 may be modified so that the results of steps S41 to S49 are used in step S30.
After execution of steps S41 to S49, the category-difference feature word extraction unit 44 determines the number of documents df (t, tgtDocs) in which words appear in the comparison range tgtDocs calculated in step S45 similar to step S25, and is similar to step S29. Based on the number of documents df (t, cmp) in which the word appears in each comparison target cmp calculated in step S49, the difference feature for each comparison target cmp for all feature words t included in the document within the comparison range tgtDocs. The degree diff (t, cmp) is calculated (S50).
Specifically, as the difference feature diff (t, cmp), based on the number of documents df (t, tgtDocs) and df (t, cmp), as shown in Table 7, common parameters a ″, b After calculating “, c”, d ”, and n”, the difference feature diff (t, cmp) is set as any statistical index as shown in Table 8. Here, for example, the T statistic is used as the difference feature. A case where the degree is diff (t, cmp) will be described.
The difference feature diff (t, cmp) is the average of the appearance frequency between the comparison target cmp_i and the difference set cmpDocs_i obtained by removing the comparison target cmp_i from the comparison range tgtDocs for the word t using the T statistic. It is an index for calculating significance based on the difference. As a result, in the comparison target cmp_i, it is possible to extract words that appear significantly more frequently as different feature words than the comparison range (cmpDocs_i) other than the comparison target. The T statistic (also referred to as T score) is described in Non-Patent Document 3, for example. Further, instead of the T statistic, a statistical index such as the log likelihood ratio LLR, the chi-square value, or the self mutual information MI described in the description of the calculation of the feature degree may be used.
In such a statistical index, the appearance frequency df (t, docAll) of each feature word t in the entire document set docAll and the appearance frequency df (t, cat) of each feature word in each category cat may be used.
The category-difference feature word extraction unit 44 selects, for each target set cmp included in the comparison target set tgtSet, the feature words having the top r difference feature degrees diff (t, cmp) calculated in step S50. The difference feature word diffTerms (cmp) is sent to the user operation / presentation unit 50 (S51). Here, the upper number r is the set value described above. In addition, although the feature word having the difference feature degree diffTerms (cmp) within the top r is set as the difference feature word, the feature word is not limited thereto, and a feature word having the difference feature degree diffTerms (cmp) equal to or greater than the threshold value is set as the difference feature word. Also good.
As shown in FIG. 12, the difference / common feature word presentation unit 53 presents each r category difference feature words sent in step S51 in the lists Ldif1 and Ldif2.
(Operation of related category presentation unit 54: FIG. 13)
For example, the related category presenting unit 54 selects a target word set tgtTerms including a plurality of category different feature words by a user operation during presentation of the category different feature words by the different / common feature word presenting unit 53, for example. Then, the degree of association rel (cat, tgtTerms) between the target word set tgtTerms that has received the selection and the category feature words in the category feature word storage unit 30 is calculated, and the category having the high degree of association rel (cat, tgtTerms). The category number data associated with the category ID associated with the feature word is highlighted (S61 to S66).
Specifically, the related category presenting unit 54 acquires a focused word set tgtTerms composed of a plurality of words selected by the user (S61). The words included in the focused word set are not limited to the category difference feature words described above, and category feature words, category common feature words, and the like can be selected as appropriate.
The related category presentation unit 54 acquires all the category feature words 33ct from the feature word storage unit 30 (S62).
The related category presentation unit 54 repeats the processing of step S64 and step S65 for all category data cat (S63).
The related category presentation unit 54 sorts the feature words included in the category feature word 33ct of the category data cat by the feature degree, and obtains the feature word ranking termRnk (S64).
The related category presenting unit 54 obtains the degree of association rel (cat, tgtTerms) between the category cat and the focused word set tgtTerms based on the focused word set tgtTerms and the feature word ranking termRnk (S65).
As the relevance rel (cat, tgtTerms), a statistical index called average accuracy can be used. This statistical index is an index that takes a higher value as the number of words included in the focused word set tgtTerms appears higher in the feature word ranking termRnk. Details of the average accuracy are described in Non-Patent Document 2. As the relevance level rel (cat, tgtTerms), in addition to the average accuracy, in the category feature word of the category cat, the value of the feature score score (t, cat) of the word t existing in the focused word set tgtTerms may be added. Good.
Based on the relevance level rel (cat, tgtTerms) of each category calculated in step S65, the related category presentation unit 54 associates the relevance level rel (cat, tgtTerms) with a category feature word having a threshold value s or more. As the category ID is the related category relCats of the focused word set tgtTerms, the cell of the category number data related to the category ID included in the related category relCats is highlighted (S66).
Note that the related category is not limited to a category having a relevance level rel (cat, tgtTerms) equal to or higher than a threshold value, and may be a category having a relevance level rel (cat, tgtTerms) within the top t. The threshold value s and the upper number t may be set in advance as in the case of the upper number r described above, or may be set every time a related category is presented.
(Operation of User Operation / Presentation Unit 50: FIG. 14)
Next, the operation of the user operation / presentation unit 50 that uses the processing such as the feature word extraction unit 40 and the related category presentation unit 54 as described above in accordance with the user operation will be described. It is assumed that the operations of the document feature word extraction unit 41 and the category feature word extraction unit 42 (steps S1 to S4 and S11 to S20) have been completed in advance.
The user operation / presentation unit 50 generally displays the category number data in a cell for each category ID, and presents the category feature word, the category common feature word, and the category difference feature word according to the selection operation by the user. Moreover, the process which emphasizes and shows a related category is performed (S100-S131).
Specifically, in the user operation / presentation unit 50, the screen presentation unit 51 displays each category as one cell for all category data stored in the feature word storage unit 30 (S100).
An example of this display is a cross display as shown in the screen G10 in FIG. In this example, the document data is a patent document as shown in FIG. 2, and there are two categories of the attribute value of the applicant 14d of the patent document and the attribute value of the application year that is the upper 4 digits of the filing date 13d. Assume a document set pre-classified with attribute values. It is assumed that the user is in the process of investigating technical trends of competitors from patent documents. In the cross display of the screen G10, one cell corresponds to one category. For example, the cell c1 corresponds to a category including patent documents filed in 2004 by Company F. Note that the display format is not limited to cross display, and any display format such as graph display, map display, or folder display can be used.
Steps S <b> 110 to S <b> 112 show processing of the category feature word presentation unit 52 that accepts selection of a category by the user and presents category feature words in the category.
That is, the category feature word presentation unit 52 performs the processing of steps S111 and S112 when the user selects the category cat for the category displayed as a cell in step S100 (S110).
The category feature word presentation unit 52 acquires category feature word data related to the category ID of the category cat selected by the user from the feature word storage unit 30 (S111).
The category feature word presentation unit 52 presents the top r feature words having the feature score (t, cat) as category feature words to the user based on the feature degrees included in the acquired category feature word data (S112). .
For example, as shown in FIG. 15, when the user selects a cell (category) c2 by clicking the mouse or the like, category feature words for the category are displayed in the list L2. Thereby, the user can grasp | ascertain the characteristic of the content of the literature contained in the selected cell c2. That is, since the technical terms “search” and “Web” appear significantly in the list L2 of category feature words in the 2005 application patent of Company F corresponding to the selected cell c2, As a focus technology in 2005, it is possible to grasp that there are search and Web. If there is another selected cell c3, the user can similarly grasp the category feature word in the analysis axis of the application year and the company name from the list L3.
Steps S120 to S126 accept the selection of the comparison target set tgtSet by the user, send the selected comparison target set tgtSet to the feature word extraction unit 40, and the common feature words and the different feature words extracted by the feature word extraction unit 40 The process of the difference / common feature word presentation unit 53 that receives and presents to the user is shown.
That is, the difference / common feature word presentation unit 53 performs the processes of steps S121 to S126 when a plurality of comparison targets are selected as the comparison target set tgtSet for the category displayed in step S100 (S120).
The difference / common feature word presentation unit 53 sends the comparison target set tgtSet to the feature word extraction unit 40 (S121). In the feature word extraction unit 40, the category common feature word extraction unit 43 executes the processes of steps S 21 to S 31 described above, and sends the obtained common feature word comTerms to the difference / feature word presentation unit 53.
The difference / common feature word presentation unit 53 receives the common feature word comTerms from the feature word extraction unit 40 and presents it to the user (S122).
The difference / common feature word presentation unit 53 sends the comparison target set tgtSet to the feature word extraction unit 40 (S123). In the feature word extraction unit 40, the category different feature word extraction unit 44 executes the processes of steps S 41 to S 51 described above, and sends the obtained different feature word diffTerms to the difference / feature word presentation unit 53.
The difference / common feature word presentation unit 53 acquires the difference feature word diffTerms from the feature word extraction unit 40 (S124).
The difference / common feature word presentation unit 53 repeats the process of step S126 for all comparison target cmp included in the comparison target set tgtSet (S125).
The difference / common feature word presentation unit 53 presents to the user the difference feature word diffTerms (cmp) for the comparison target cmp (S126).
Display examples in steps S120 to S126 are as shown in FIG. For example, when the user wants to compare the technical trends of Company A and Company B, the user selects one comparison target by selecting a plurality of cells indicating categories related to Company A on the screen G10 as indicated by a solid line frame f1. .
As another comparison object, when a plurality of cells indicating categories relating to the company B are selected as indicated by the dotted line frame f2, in the comparison range constituted by the two comparison objects indicated by the solid line frame f1 and the dotted line frame f2. The common feature word list Lcom is displayed.
In this way, “classification” and “clustering” are presented as technical terms that appear significantly in the common feature word list Lcom in the patent applications of both companies, and these technologies are technical fields common to Company A and Company B. I can understand that.
Further, the different feature word list Ldif1 for the comparison target indicated by the solid line frame f1 is displayed, and the different feature word list Ldif2 for the comparison target indicated by the dotted line frame f2 is displayed. By displaying such difference feature word lists Ldif1 and Ldif2, it is possible to grasp the technology representing the uniqueness of the A company and the B company.
In addition, by displaying common feature words and different feature words for a plurality of comparison targets, it is possible to show the features between comparison targets more clearly to the user than simply presenting feature words for a document set. .
Steps S130 and S131 accept a focused word set tgtTerms consisting of focused words selected from these feature words during the presentation of each feature word in steps S112, S122, and S126. The process of the related category presentation part 54 which calculates a relevance level and presents a category having a high relevance level to the user as a related category is shown.
When the user selects a word of interest from the category feature word, common feature word, or different feature word presented by the category feature word presentation unit 52 or the different / common feature word presentation unit 53, The process of step S131 is performed (S130). Here, it is assumed that the user can select a plurality of words as the attention word, and the selected plurality of attention words is the attention word set tgtTerms. Moreover, although this embodiment demonstrated the case where a focused word was selected from the feature word shown, it is not restricted to this, A user may input arbitrary keywords as a focused word like the search of Web.
The related category presenting unit 54 calculates the degree of association between the focused word set tgtTerms and each category by executing the processing of steps S61 to S66 described above, and presents the category having a high degree of association to the user as a related category ( S131).
For example, as shown in FIG. 16, the user selects the words Tcom1 and Tdif2 to be focused on from the common feature word list Lcom and the different feature word lists Ldif1 and Ldif2. Here, when the user is interested in the words “classification” and “XML”, the user selects the common word Tcom1 indicating the word “classification” and the different feature word Tdif2 indicating the word “XML” as the attention words. . Upon receiving the user's selection of the word of interest, the related category presentation unit 54 highlights the cell c4 of the related category having a high degree of association with the word of interest by changing the background color or the like.
As a result, the user can grasp the clue of the range to be investigated for the technology of interest. In the example shown in FIG. 16, it is understood that “Category” and “XML” focused on by the user are related to these technologies from the viewpoint of the company. Furthermore, from the viewpoint of the filing year, it can be seen that patents related to these technologies have appeared significantly between 2006 and 2008. As a result, the user can clarify the range to be investigated in detail for the technology of interest, and can efficiently perform prior art searches.
The attention word can be selected not only from common feature words and different feature words but also from category feature words. For example, as shown in FIG. 17, in addition to the selection of the attention word, when the attention word is selected from the category feature words in the category feature word list L2 in the cell c2, the related category is displayed according to this selection. Change.
The screen presentation unit 51 ends the process when the user selects the end of the system, and otherwise returns the process to step S110 (S140).
For example, an example in which the process is returned to step S110 and the survey is continued will be described. FIG. 18 is a diagram illustrating a display example of narrowing down comparison targets, common feature words, and different feature words. The user can make changes such as reduction (narrowing) or enlargement of the comparison target in response to the presentation of the common feature word, the difference feature word, the category feature word, and the related category for the designated word of interest.
For example, as shown in FIG. 16, during the presentation of feature words and related word categories, the user narrows down the frames f1 and f2 of the comparison range to 2006-2008 for the application year as shown in FIG. Company C is selected as a comparison company by the dotted line frame f3. Based on the comparison target set tgtSet based on each of the frames f1 to f3, the feature word extraction device changes the common feature word or the different feature word to be presented. As a result, the user becomes a clue to discover technical terms to which attention should be paid, although the user has not previously thought of them.
FIG. 19 is a diagram illustrating a display example of a related word change (addition / deletion) by a user and related categories. The user can add or delete the word of interest while viewing the common feature word, the difference feature word, or the category feature word.
For example, in response to the change of the feature word shown in FIG. 18 or the difference feature word of company C, a different feature word Tdif3 indicating the word “mining” is newly added to the attention word. In response to this, the feature word extraction device changes the related category to be presented.
Thereby, the user can discover the relation between the attention word and the category, which has not been noticed before, by overviewing the related categories while switching the attention words. If it is a prior art search, an unexpected company dealing with the technology of interest, or a certain company has applied for a patent related to the technology of interest from an early age than the user is aware A clue to discover.
Further, as described with reference to FIGS. 15 to 19, “presentation of feature words” and “presentation of related categories” by the feature word extraction device, “designation of comparison target” and “selection of attention word” by the user By repeating the process, it is possible to clarify the object to be analyzed and the characteristic word, and to discover the keyword and the object to be analyzed that the user has not been aware of before. In patent research, it will be a clue to discover new technologies to focus on and competitors to watch out for. In addition, by using the comparison target and the attention word, it is possible to realize a prior art search using an appropriate word for an appropriate comparison range.
For example, as shown in FIG. 20, the cross analysis screen G10 with the analysis axis of “time series × company”, as shown in FIG. 21 and FIG. 22, shows the cross analysis with the analysis axis of “time series × feature word” for a certain company. Screen G20, cross-analysis screen G30 with the analysis axis of "time series x company" for a certain feature word, etc. Applying to the screen of any analysis axis, analysis and investigation with appropriate comparison range and appropriate word Can be realized.
Further, for example, the screen G30 of the “time series × company” cross analysis for a certain feature word is changed to a screen display G31 of a “time series × company” graph display for a certain feature word as shown in FIG. Can do. The display format can be changed in the same manner on the other cross analysis screens G10 and G20.
As described above, according to the present embodiment, during the presentation of the category number data including the number of document IDs associated with the category ID, among the category feature words related to the category ID of the category number data that has been selected, A document feature word in a category feature word having a higher feature degree is presented as a category feature word.
In addition, when the selection of the comparison target set consisting of a plurality of comparison targets that is the union of the category IDs of the plurality of category number data is accepted during the presentation of the category number data, each category in the comparison target set that has received the selection For each document feature word associated with each document ID associated with the ID, a difference feature degree of a degree representing the difference of the document feature word in each comparison target is calculated, and a document feature word having a higher difference feature degree is categorized. Present as a difference feature word.
Supplementally, by presenting the category feature words, the user can confirm the category feature words for each category, and can efficiently grasp the overall image of the document set and the contents of each category.
Moreover, the structure which presents the difference characteristic word between several comparison object WHEREIN: The user can grasp | ascertain the difference of the comparison object in the arbitrary ranges which are paying attention. Furthermore, if the comparison target set is narrowed down, the differences in each comparison target can be grasped in more detail. On the other hand, if the comparison target set is expanded, the difference can be grasped from a macro viewpoint, and the overall content can be deepened. As described above, the user can understand the contents of the document set while referring to each feature word, and can clarify the range to be analyzed and the keyword to be noted.
Further, according to the present embodiment, during the presentation of the category number data, when the selection of the comparison target set composed of a plurality of comparison targets that is the union of the category IDs of the plurality of category number data is accepted, the selection is accepted. For each document feature word associated with each document ID associated with each category ID in the comparison target set, a common feature degree that indicates the commonality of the document feature words in the comparison target set is calculated, and the common feature degree By presenting higher-level document feature words as category common feature words, the user can grasp the common points of documents in the range that he / she is paying attention to, and the understanding of the document set is further deepened. You can catch keywords more clearly.
Furthermore, according to the present embodiment, for example, when a selection of a focused word set made up of a plurality of category-difference feature words is accepted during presentation of a category-difference feature word, the focused word set that has received the selection and a category feature-word storage unit By calculating the degree of association with the category feature word in 30 and highlighting the category number data associated with the category ID associated with the category feature word having a high degree of association, the user can select the attention word By overviewing the presented categories, it is possible to grasp the categories related to the keyword that the user is paying attention to, and to discover analysis targets that were not noticed before. Therefore, the user can grasp an appropriate analysis target for matters to be analyzed, and can perform analysis with higher accuracy.
In this way, the user repeats selection (narrowing or expanding) and selection of the target word while confirming the difference feature word / common feature word and the related category, thereby selecting the range to be analyzed and the feature word of interest. It can be clarified. As a result, the user can efficiently perform content grasping and comparison surveys for a plurality of document sets without omission and without waste.
Note that the method described in the above embodiment includes a magnetic disk (floppy (registered trademark) disk, hard disk, etc.), an optical disk (CD-ROM, DVD, etc.), a magneto-optical disk (MO) as programs that can be executed by a computer. ), And can be distributed in a storage medium such as a semiconductor memory.
Further, the storage medium in the present invention is not limited to a medium independent of a computer, but also includes a storage medium in which a program transmitted via a LAN, the Internet, or the like is downloaded and stored or temporarily stored.
Further, the number of storage media is not limited to one, and the case where the processing in the above embodiment is executed from a plurality of media is also included in the storage media in the present invention, and the media configuration may be any configuration.
The computer according to the present invention executes each process in the above-described embodiment based on a program stored in a storage medium, and is a single device such as a personal computer or a system in which a plurality of devices are connected to a network. Any configuration may be used.
In addition, the computer in the present invention is not limited to a personal computer, but includes a processing unit, a microcomputer, and the like included in an information processing device, and is a generic term for devices and devices that can realize the functions of the present invention by a program. .
DESCRIPTION OF SYMBOLS 10 ... Document memory | storage part, 20 ... Category memory | storage part, 30 ... Feature word memory | storage part, 40 ... Feature word extraction part, 41 ... Document feature word extraction part, 42 ... Category feature word extraction part, 43 ... Category common feature word extraction part 44 ... category difference feature word extraction unit, 50 ... user operation / presentation unit, 51 ... screen presentation unit, 52 ... category feature word presentation unit, 53 ... difference / common feature word presentation unit, 54 ... related category presentation unit.
Document storage means for storing a plurality of documents having a document ID and content text information;
Category storage means for storing one or more document IDs in association with each category ID;
Document feature word storage means for storing the document ID of the document and the document feature word extracted from the content text information of the document in association with each other in the document storage means;
Category feature word storage means for associating and storing a category ID and one or more document IDs related in the category storage means and a category feature word related to the category ID;
Morphological analysis of the content text information for each document in the document storage means, extracting a document feature word from the result of the morpheme analysis, the extracted document feature word, and the document ID of the document corresponding to the document feature word Document feature word extraction means for writing to the document feature word storage means in association with each other,
An appearing document number calculating unit that receives the document set composed of the plurality of documents and calculates the number of documents in which the document feature word related to the document ID included in the document set appears in the document with the document ID; ,
Feature degree calculating means for calculating the feature degree of the document feature word for the documents related to the category ID in all documents, based on the number of documents calculated by the appearance document number calculating means;
A category feature word is created by adding the feature level to the document feature word, and the category feature word is stored by associating the created category feature word with a category ID and one or more document IDs related to the category feature word. A category feature word creation means for writing to the means;
Category number presentation means for presenting category number data including the number of document IDs related to the category ID for each category ID in the category storage means;
Category number data selection accepting means for accepting selection of any category number data during presentation of the category number data by the category number presenting means;
Category feature word presentation that presents a document feature word in a category feature word having a higher feature degree as a category feature word among category feature words related to a category ID of category number data that has been selected by the category number data selection accepting means Means,
A comparison target set selection receiving means for receiving selection of a comparison target set consisting of a plurality of comparison targets that is a union of the category IDs of the plurality of category number data during the presentation of the category number data by the category number presentation means;
Based on the number of documents calculated by the appearance document number calculating unit for each document feature word related to each document ID associated with each category ID in the comparison target set that has been selected by the comparison target set selection receiving unit. A category difference feature word sending means for calculating a difference feature degree of a degree representing the difference between the document feature words in each comparison target, and sending a document feature word having a higher difference feature degree as a category difference feature word;
Category difference feature word presenting means for presenting the category difference feature word sent by the category difference feature word sending means;
A feature word extraction device characterized by comprising:
In the feature word extraction device according to claim 1,
The appearance document number calculating means includes:
For each document feature word in the document storage means, the number of appearance document number calculation means for calculating the number of documents in which the document feature word appears in all the documents of the document storage means;
For each category ID in the category storage means, a document feature word related to the document ID associated with the category ID, and a document document occurrence word number calculating means for calculating the number of documents that appear in the document with the document ID; ,
For each document feature word related to each document ID associated with each category ID in the comparison target set that has been selected by the comparison target set selection receiving means, the document in all documents related to the document ID First document number calculating means for calculating the number of documents in which a feature word appears;
For each comparison target in the comparison target set that has been selected by the comparison target set selection receiving means, a document feature word associated with each document ID associated with each category ID in the comparison target is a document with that document ID. Second document number calculating means for calculating the number of documents appearing in the document;
In the feature word extraction device according to claim 1 or 2,
The number of documents calculated by the first document number calculation means and the second document number calculation for each document feature word related to each document ID associated with each category ID in the comparison target set that has received the selection Based on the number of documents calculated by the means, a common feature degree of a degree representing commonality of the document feature words in the comparison target set is calculated, and a document feature word having a higher common feature degree is set as a category common feature word. A category common feature word sending means for sending;
Category common feature word presenting means for presenting category common feature words sent by the category common feature word sending means;
A feature word extraction device further comprising:
During the presentation of category different feature words by the category number presenting means, attention word set selection receiving means for receiving selection of a target word set made up of a plurality of category different feature words;
The degree of association between the target word set received by the target word set selection receiving unit and the category feature word in the category feature word storage unit is calculated, and the category ID associated with the category feature word having a high degree of association is obtained. Related category presentation means for highlighting related category count data;
A program for a feature word extraction device comprising a document storage means, a category storage means, a document feature word storage means, and a category feature word storage means,
The feature word extraction device,
Document writing means for writing a plurality of documents having a document ID and content text information into the document storage means;
Category writing means for associating one or more document IDs for each category ID and writing them in the category storage means;
For each document feature word in the document storage means, the number of appearance document number calculation means for calculating the number of documents in which the document feature word appears in all the documents in the document storage means;
For each category ID in the category storage means, a document feature word related to the document ID associated with the category ID calculates the number of documents that appear in the document with the document ID,
Based on the number of documents calculated by the number-of-appearance document number calculating means in all the documents and the number of documents calculated by the number-of-category document number calculating means, the document for the document related to the category ID in all documents Feature degree calculating means for calculating the feature degree of the feature word;
A category feature word is created by adding the feature level to the document feature word, and the category feature word is stored by associating the created category feature word with a category ID and one or more document IDs related to the category feature word. Category feature word creation means to be written in the means,
Category number presentation means for presenting category number data including the number of document IDs associated with the category ID for each category ID in the category storage means;
Category number data selection accepting means for accepting selection of any category number data during presentation of the category number data;
For each document feature word associated with each document ID associated with each category ID in the comparison target set that has been selected by the comparison target set selection receiving means, the number of documents calculated by the first document number calculation means Based on the number of documents calculated by the second number-of-documents calculation means, a difference feature degree of a degree representing the difference of the document feature word in each comparison target is calculated, and the document feature word having a higher difference feature degree A category difference feature word sending means for sending as a category difference feature word;
A category difference feature word presenting means for presenting a category difference feature word sent by the category difference feature word sending means;
JP2010064821A 2010-03-19 2010-03-19 Feature word extraction apparatus and program Active JP5023176B2 (en)
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JP2010064821A Active JP5023176B2 (en) 2010-03-19 2010-03-19 Feature word extraction apparatus and program
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