JP2022029461A - Keyword extraction device, keyword extraction method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a keyword extraction device, keyword extraction method and program, which improve data collection efficiency of users.

SOLUTION: A keyword extraction device is provided, comprising a processing unit configured to extract candidates of keywords contained in a document of interest as keywords on the basis of the number of similar documents, similar to the document of interest, containing the candidates of keywords that appears in the document of interest.

SELECTED DRAWING: Figure 4

COPYRIGHT: (C)2022,JPO&INPIT

Description

The present invention relates to a keyword extraction device, a keyword extraction method, and a program.

Documents such as news articles distributed on the Internet include many related documents such as follow-up articles that presuppose the matters of documents distributed in the past. In connection with this, a technique for distributing a follow-up article related to the matter of an article distributed in the past is known (see, for example, Patent Document 1).

Japanese Unexamined Patent Publication No. 2005-242758

However, in the conventional technique, when a user searches for a document, the related document related to the document cannot be searched accurately, and as a result, the efficiency of information collection by the user may be lowered.

The present invention has been made in view of the above problems, and an object of the present invention is to provide a keyword extraction device, a keyword extraction method, and a program capable of improving the efficiency of information collection by a user.

One aspect of the present invention is a candidate for the keyword included in the document of interest based on the number of similar documents in which the candidate for the keyword appearing in the document of interest appears among a plurality of similar documents similar to the document of interest. Equipped with a processing unit that extracts
It is a keyword extractor.

According to one aspect of the present invention, the efficiency of user information collection can be improved.

It is a figure which shows an example of the information processing system 1 including the information processing apparatus 100 in 1st Embodiment. It is a figure which shows an example of the web page provided by the service providing apparatus 20. It is a figure which shows an example of a related page. It is a figure which shows an example of the structure of the information processing apparatus 100 in 1st Embodiment. It is a flowchart which shows the flow of a series of processing by the information processing apparatus 100 in 1st Embodiment. It is a figure which shows an example of the classification result of a document. It is a figure which shows an example of the evaluation result of the keyword extractor EX. It is a figure which shows an example of the structure of the information processing apparatus 100A in 2nd Embodiment. It is a flowchart which shows the flow of a series of processing by the information processing apparatus 100A in 2nd Embodiment. It is a figure which shows an example of the usage scene of the keyword extracted by a keyword extractor EX. It is a figure which shows an example of the hardware composition of the information processing apparatus 100, 100A of embodiment.

Hereinafter, the keyword extraction device, the keyword extraction method, and the program to which the present invention is applied will be described with reference to the drawings.

[Overview]
The information processing device is realized by one or more processors. The information processing device includes a plurality of documents in which related documents are manually classified into the same group, and keywords extracted by a keyword extractor that extracts keywords having predetermined characteristics in the documents from a plurality of documents. get. When the information processing apparatus acquires a plurality of documents and keywords, the information processing apparatus evaluates the performance of the keyword extractor based on the degree of matching of the keywords between the documents in the group. The higher the performance of the keyword extractor, the more the keywords extracted by the keyword extractor represent the original meaning and concept of the document. When a document is searched using such a keyword, the keyword extractor can search for a document related to the document for which the keyword is extracted. As a result, the efficiency of user information collection can be improved.

<First Embodiment>
[overall structure]
FIG. 1 is a diagram showing an example of an information processing system 1 including an information processing apparatus 100 according to the first embodiment. The information processing system 1 in the first embodiment includes, for example, one or more terminal devices 10, a service providing device 20, and an information processing device 100. These devices are connected via the network NW.

Each device shown in FIG. 1 transmits and receives various information via the network NW. The network NW includes, for example, the Internet, a WAN (Wide Area Network), a LAN (Local Area Network), a provider terminal, a wireless communication network, a wireless base station, a dedicated line, and the like. It should be noted that not all combinations of the devices shown in FIG. 1 need not be able to communicate with each other, and the network NW may include a local network in part.

The terminal device 10 is a terminal device including an input device, a display device, a communication device, a storage device, and an arithmetic unit, such as a mobile phone such as a smartphone, a tablet terminal, and various personal computers. The communication device includes a network card such as a NIC (Network Interface Card), a wireless communication module, and the like. In the terminal device 10, a UA (User Agent) such as a web browser or an application program is activated, and a request according to the content input by the user is transmitted to the service providing device 20. Further, the terminal device 10 in which the UA is activated causes the display device to display various images based on the information acquired from the service providing device 20.

The service providing device 20 is, for example, a web server that provides a web page to the terminal device 10 in response to a request from a web browser. The web page is, for example, a web page (hereinafter referred to as a search page) that provides a search service. The search page includes, for example, documents (text data) such as news articles, and contents such as moving image data, still image data, and audio data. Further, the service providing device 20 may provide the terminal device 10 with a web page that provides various services such as Internet shopping, SNS (Social Networking Service), and mail service. Further, the service providing device 20 may be an application server that provides the content to the terminal device 10 in response to a request from the application program.

For example, when the user inputs a query to the search page displayed on the terminal device 10, the service providing device 20 displays a web page in which the word or phrase corresponding to the query is included in the document. , Is provided to the terminal device 10 as a search result by a query. In response to this, when the user selects a desired web page from the search results, the service providing device 20 includes the keyword extracted from the web page selected by the user in the web page, and the terminal device 10 To provide to. The service providing device 20 may extract keywords in advance from the web page to be provided by using the keyword extractor EX described later, or extract the keywords from the web page to be provided at the time of searching. May be good.

FIG. 2 is a diagram showing an example of a web page provided by the service providing device 20. When a web page containing news articles related to the Olympic Games is provided as shown in the example shown in the figure, the web pages include "○○ Olympics" and "○○ Olympics" extracted from the news articles. Keyword KW such as "□□□□ player" is included. A URL (Uniform Resource Locator) for accessing a search result using the keyword KW as a query is linked to the keyword KW. Therefore, it is preferable that the keyword KW simply expresses the content of the document, and further, when the keyword is used as a query, it is preferable that another document can be easily searched. In this way, by providing the user with a web page including a link destination to the search result of the keyword KW, the user can also acquire information related to the information searched by himself / herself. Hereinafter, a web page obtained by searching for the keyword KW as a query is referred to as a "related page".

FIG. 3 is a diagram showing an example of related pages. In the illustrated example, the search result when the keyword KW "○○ Olympics" illustrated in FIG. 2 is searched as a query is shown. In such search results, titles, URLs, summaries (snippets), images, etc. of each related page related to the "○○ Olympics" are posted as items (lists). In the illustrated example, the top related page contains the document A, the second related page contains the document B, and the third related page contains the document C. .. Each of these related pages contains a document that is a collection of words and phrases that are candidate keywords. The document included in the related page (hereinafter referred to as the related document) and the document from which the keyword is extracted have the property of sharing the same keyword (keyword or key phrase sharing). The higher the shareability of the key phrase, that is, the greater the number of shares of the same keyword, the more related pages can be provided to the user.

The information processing apparatus 100 evaluates the keyword extractor EX used by the service providing apparatus 20 by comparing the keywords extracted from each document by the keyword extractor EX.

[Information processing device configuration]
FIG. 4 is a diagram showing an example of the configuration of the information processing apparatus 100 according to the first embodiment. As shown in the figure, the information processing apparatus 100 includes, for example, a communication unit 102, a control unit 110, and a storage unit 130.

The communication unit 102 includes, for example, a communication interface such as a NIC. The communication unit 102 communicates with the terminal device 10, the service providing device 20, and the like via the network NW.

The control unit 110 includes, for example, a keyword assignment unit 112, a document classification unit 114, and an extractor evaluation unit 116. These components are realized by, for example, a processor such as a CPU (Central Processing Unit) executing a program stored in the storage unit 130. Further, some or all of the components of the control unit 110 are hardware such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or GPU (Graphics Processing Unit). It may be realized by (circuit unit; circuitry), or it may be realized by the cooperation of software and hardware.

The storage unit 130 is realized by, for example, a storage device such as an HDD (Hard Disc Drive), a flash memory, an EEPROM (Electrically Erasable Programmable Read Only Memory), a ROM (Read Only Memory), and a RAM (Random Access Memory). In addition to various programs such as firmware and application programs, the storage unit 130 stores keyword extractor data 132 and document data 134.

The keyword extractor data 132 is information (program) that defines what kind of extractor the keyword extractor EX is, and may be, for example, information that defines each of a plurality of keyword extractors EX. For example, the keyword extractor EX divides a document containing a plurality of words or phrases into a plurality of morphemes that are candidate for keywords by morphological analysis, and the appearance of words such as TF (Term Frequency) -IDF (Inverse Document Frequency). Using a method for evaluating frequency, each divided morpheme or a plurality of morphemes are weighted for a combination, and the one with a large weight is extracted as a keyword. Weights based on TF-IDF are an example of "predetermined features".

For example, the keyword extractor EX does not limit the number of morphemes as keyword candidates to one, but may extract keywords having a predetermined length by setting a predetermined number (for example, three). Further, for example, the keyword extractor EX may limit the morphemes that are candidates for keywords to specific part of speech such as nouns, adjectives, and verbs. Further, for example, the keyword extractor EX may limit the morphemes that are candidates for keywords to half-width characters or full-width characters, or if it is an alphabet, it may be limited to lowercase letters. Further, for example, the keyword extractor EX may convert the inflected form of the part of speech that is a candidate for the keyword into a predetermined inflected form. Specifically, when the auxiliary verb is used as a keyword candidate as a morpheme, the keyword extractor EX may convert the conjugation of the auxiliary verb from the "desu, masu" key to the "is" key. In this way, it is the designer of the keyword extractor EX that specifies the length of the keyword, limits the type of part of speech, specifies full-width half-width and uppercase and lowercase letters, and specifies the conjugation of part of speech. For example, it is treated as a hyperparameter determined in advance by a system engineer).

The document data 134 is data including a plurality of documents (for example, 10,000 documents), and may include, for example, a document included in each web page provided by the service providing device 20, or is prepared separately. The document may be included. The document data 134 may comprehensively include documents of various genres and themes, or may include only documents of a specific genre or theme. The number of documents corresponding to each genre or theme does not have to be equal, and there may be bias such as many documents of a specific genre or theme and few documents of other genres or themes. ..

[Processing flow]
Hereinafter, the flow of a series of processes by the information processing apparatus 100 in the first embodiment will be described according to a flowchart. FIG. 5 is a flowchart showing a flow of a series of processes by the information processing apparatus 100 in the first embodiment. The processing of this flowchart may be repeated, for example, at a predetermined cycle.

First, the keyword assigning unit 112 constructs (generates) one or a plurality of keyword extractors EX based on the keyword extractor data 132, and the constructed keyword extractor EX is used from each document included in the document data 134. One or more keywords are extracted, and the extracted keywords are added to the extraction source document (S100). The keyword giving unit 112 is an example of the “acquisition unit”. Further, the keyword extractor EX constructed by the keyword assigning unit 112, that is, the keyword extractor EX realized by the processor referring to the keyword extractor data 132 is an example of the “keyword extractor”.

Next, the document classification unit 114 controls the communication unit 102 to transmit a plurality of documents to which the keyword is added by the keyword assigning unit 112 to the predetermined terminal device 10, and the document is sent to the user of the predetermined terminal device 10. Request grouping (S102). The predetermined terminal device 10 may be, for example, a computer of a participant of crowdsourcing. A user who has been requested to group documents, for example, looks at a plurality of documents, classifies documents having related contents into the same group, and transmits the classification result to the information processing device 100 using the terminal device 10. do.

FIG. 6 is a diagram showing an example of a document classification result. The document ID in the figure represents the identification information of each of the plurality of documents requested to be grouped. In the illustrated example, a document having a document ID of "DOC_A" (hereinafter, document A), a document having a document ID of "DOC_B" (hereinafter, document B), and a document having a document ID of "DOC_C" (hereinafter, document C). ) Is classified into group X related to baseball, and a document having a document ID of "DOC_D" (hereinafter referred to as "Document D") and a document having a document ID of "DOC_E" (hereinafter referred to as "Document E") are related to soccer. It is classified into the group Y. It should be noted that two keywords are extracted from each document, and the number of extracted keywords is assumed to be predetermined as a hyperparameter.

Next, the extractor evaluation unit 116 waits until the communication unit 102 acquires the classification results of a plurality of documents from the predetermined terminal device 10 (S104), and the communication unit 102 acquires the classification results of the plurality of documents. , One document of interest (hereinafter referred to as the document of interest) is selected from a plurality of documents (population) classified into groups (S106).

Next, the extractor evaluation unit 116 compares one or more keywords extracted from the selected document of interest with one or more keywords extracted from other documents classified in the same group as the document of interest. The F value (F-measure) is derived based on the degree to which the keywords match each other. The F value may be derived by the harmonic mean of the precision and the recall. For example, the extractor evaluation unit 116 derives the F value based on the mathematical formula (1), derives the precision ratio based on the mathematical formula (2), and derives the recall rate based on the mathematical formula (3).

R in the above formula represents the number of other documents in which the same keyword as the document of interest is extracted from among one or more other documents classified in the same group as the document of interest, and N is a plurality of documents requested to be grouped. Of the remaining other documents excluding the document of interest, the number of other documents in which the same keyword as the document of interest is extracted is represented, and C represents the number of other documents classified in the same group as the document of interest. There is.

In the case of the classification result exemplified in FIG. 6, when the document A is the document of interest, the extractor evaluation unit 116 is, for example, among the other documents (B, C) classified into the same group as the document A, " The quotient between the number R of documents from which the keyword "baseball" is extracted and the number N of documents from which the keyword "baseball" is extracted from other documents (B to E) excluding document A regardless of the group. It is derived as the conformance rate of the keyword "baseball" extracted from the document A. In the case of the example of FIG. 6, since the keyword "baseball" is extracted from the document D of the group Y, N becomes 1, and the keyword "baseball" is not extracted from each document of the group X. Therefore, R Is 0. Therefore, the precision ratio is 0/1, that is, 0 [%].

Further, the extractor evaluation unit 116 is classified into the same group as the document A and the number R of the documents from which the keyword "baseball" is extracted from the other documents (B, C) included in the same group as the document A. The quotient with the number C of other documents is derived as the recall rate of the keyword "baseball" extracted from the document A. In the case of the example of FIG. 6, since two documents other than the document A are classified in the group X, C is 2, and the keyword "baseball" is not extracted from those two documents. , R becomes 0. Therefore, the reproducibility is 0/2, that is, 0 [%].

Similarly, the conformance rate of the keyword "baseball" extracted from the document A is 100 [%] because R is 2 and N is 2. Further, the recall rate of the keyword "baseball" extracted from the document A is 100 [%] because R is 2 and C is 2.

The extractor evaluation unit 116 derives an F value for each keyword extracted from the document A selected as the document of interest. The F value of each keyword extracted from the document of interest represents an evaluation value that evaluates the performance of the keyword extractor EX for each keyword. In the case of the above numerical example, the F value of the keyword "baseball" has a precision rate of 0 [%] and a reproducibility of 0 [%], so (2 x 0 [%] x 0 [%]) /. (0 [%] + 0 [%]) = 0 [%]. Further, since the F value of the keyword "baseball" has a conformance rate of 100 [%] and a reproducibility of 100 [%], (2 x 100 [%] x 100 [%]) / (100 [%]. ] + 100 [%]) = 100 [%].

Then, the extractor evaluation unit 116 derives the F value of the document of interest by averaging the F values of each keyword. The F value of the document of interest represents an evaluation value that evaluates the performance of the keyword extractor EX for the document of interest. In the case of the above numerical example, the F value of the document A is 50 [%].

Next, the extractor evaluation unit 116 determines whether or not all the documents included in the population have been selected as the documents of interest (S110), and if all the documents have not yet been selected as the documents of interest, the focus is on. The document is changed, and the processing of S106 and S108 is repeated.

For example, when the extractor evaluation unit 116 changes the document of interest from document A to document E, R is 1 and N is 2 for the matching rate of the keyword "soccer" extracted from document E. Therefore, it is derived as 50 [%], and the recall rate of the keyword "soccer" extracted from the document E is derived as 100 [%] because R is 1 and C is 1. The extractor evaluation unit 116 sets the F value of the keyword “soccer” in the document E to (2 × 50 [%] × 100 [%]) / (50 [%] + 100 [%]) ≈66.7 [%]. ] To be derived.

Further, the extractor evaluation unit 116 derives the conformance rate of the keyword "baseball" extracted from the document E as 0 [%] because R is 0 and N is 1, and is derived from the document E. The recall rate of the extracted keyword "baseball" is derived as 0 [%] because R is 0 and C is 1. The extractor evaluation unit 116 sets the F value of the keyword "baseball" in the document E as (2 x 0 [%] x 0 [%]) / (0 [%] + 0 [%]) = 0 [%]. Derived. Then, the extractor evaluation unit 116 derives 33.3 [%], which is the average of the F values of each keyword of the document E, as the F value of the document E.

In this way, the extractor evaluation unit 116 repeatedly obtains the F value of all the documents included in the population while changing the document of interest.

Next, the extractor evaluation unit 116 evaluates the keyword extractor EX based on the F value of all the documents included in the population (S112). For example, the extractor evaluation unit 116 averages the F-numbers of the documents for each group, and further averages the average F-numbers obtained for each group to evaluate the performance of the keyword extractor EX for the population. Derived as a value.

FIG. 7 is a diagram showing an example of the evaluation result of the keyword extractor EX. In the illustrated example, the evaluation results for each of the plurality of keyword extractors EX are shown. As shown in the illustrated example, when the F value of each of the documents A, B, and C classified into the group X is 50 [%], the extractor evaluation unit 116 averages the F values of the three documents. The value of 50 [%] is derived as an evaluation value for evaluating the performance of the keyword extractor EX for the group X. Further, when the F value of each of the documents D and E classified into the group Y is 33 [%], the extractor evaluation unit 116 is the average value of the F values of the two documents 33 [%]. Is derived as an evaluation value that evaluates the performance of the keyword extractor EX for the group Y.

Then, the extractor evaluation unit 116 evaluated the performance of the keyword extractor EX with respect to the population by using 42 [%], which is the average ((50 + 33) / 2) of the F value of the group X and the F value of the group Y. Derived as an evaluation value.

Next, the extractor evaluation unit 116 controls the communication unit 102 to transmit the evaluation result of the keyword extractor EX (for example, the F value for the population) to the service providing device 20 (S114). In response to this, the service providing device 20 extracts a keyword from a web page or the like by using the keyword extractor EX having the largest F value, for example, when a plurality of keyword extractors EX exist. As a result, it becomes easy to extract general-purpose keywords that can be used for searching related pages, that is, keywords that are highly shared between documents, so that more related pages can be provided to the user.

According to the first embodiment described above, a plurality of documents in which related documents are manually classified into the same group and keywords extracted from the documents by the keyword extractor EX are acquired, and between the documents in the group. Since the performance of the keyword extractor EX is evaluated based on the degree of matching of the keywords in the above, the keyword extractor EX with good performance can be used, and keywords with high commonality between documents can be extracted. Thereby, when the user searches for a document, the related document related to the document can be easily searched, and the document searched by the user can be more related and more related documents can be provided. As a result, the efficiency of user information collection can be improved.

In general, the keyword extractor EX prepares in advance correct answer data that such keywords are extracted if a human is such a document, and the correct answer data and the keyword extractor EX extract the correct answer data. Supervised learning is done based on the keywords. In such a case, assuming the document illustrated in FIG. 6, when a human sets the correct answer data (correct answer keyword) of the document classified into group X as "baseball", the keyword extractor EX "bases". If the keyword "ball" is extracted, that keyword is incorrect. Similarly, if the full name (name) of a person's name is used as the correct answer data, and if only the "last name" is extracted as a keyword or if only the "name" is extracted as a keyword, they are incorrect answers.

On the other hand, in the above-described embodiment, the keyword extractor EX extracts the keyword within the group defined by the human, instead of comparing the keyword defined by the human as the correct answer data with the keyword extracted by the keyword extractor EX. In order to compare the keywords that have been created, the keyword extractor is used from the viewpoint of whether the keywords are the same or different between documents classified into the same group, without being influenced by the semantic fluctuation of the correct answer data determined by humans. EX can be evaluated.

Further, for example, when the hyperparameters are determined so that the keyword extractor EX extracts a relatively long keyword that combines a plurality of words, the learning data is also set to the length of the keyword extracted by the keyword extractor EX. It needs to be matched. In this case, when the hyperparameters are changed to adjust the length of the keyword to be extracted by the keyword extractor EX, it is necessary to change the learning data each time, and the cost of creating the learning data tends to increase.

On the other hand, in the above-described embodiment, in order to compare the keywords extracted by the keyword extractor EX within the group defined by humans, the length of the keywords to be extracted by the keyword extractor EX by changing the hyperparameters is set. Even if they are changed, the keywords to be compared have the same length in common, and the grouping itself is not changed, so that the cost of creating learning data can be reduced.

In addition, when learning the keyword extractor EX so that the keywords to be extracted approach the correct answer data determined by humans, all the keywords specified as correct answer data may be unified into half-width characters or lowercase letters. It is necessary to perform preprocessing such as converting the conjugation of auxiliary verbs at the end of a sentence from "desu, masu" to "desu".

On the other hand, in the above-described embodiment, only the length of the keyword extracted by the keyword extractor EX and the inflected form of each part of speech are defined in advance as hyperparameters, and the above-mentioned preprocessing is omitted. Can be done.

As described above, according to the above-described embodiment, it is possible to reduce the cost (work load, etc.) required for learning by eliminating the need to create correct answer data for each document simply by grouping a plurality of documents in advance. can. Further, according to the above-described embodiment, it is not necessary to consider various concepts such as whether the keywords to be extracted are Chinese characters, foreign words such as English characters (horizontal characters), and full names. Further, according to the above-described embodiment, since the keywords extracted from other documents in the same group are used as correct answer data, keywords peculiar (unique) to the document (for example, coined words created by the document creator) are included. If this is the case, the F value will be small unless specific keywords are extracted from other documents in the same group, so it will be more difficult to use as much as the keyword extractor EX, which makes it easier to extract document-specific keywords, and sharing between documents. The keyword extractor EX, which makes it easier to extract keywords with high characteristics, is easier to use.

Further, according to the above-described embodiment, since the average of the F values of the documents is obtained for each group, the influence of the bias of the number of samples (number of documents) among the groups of the population can be suppressed. For example, if there is a bias such as many documents of a specific genre or theme and few documents of other genres or themes, the number of samples when the F value is averaged for all documents, not for each group. The F-number of the group with a large number of samples is greatly reflected in the overall evaluation value, and the keyword extractor tends to be learned so that the keyword extraction accuracy is high for the group with a large number of samples. On the other hand, in the above-described embodiment, the keyword extractor EX can be learned after eliminating the difference in the number of samples between the groups by first averaging the F values for each group. As a result, keywords with high commonality between documents can be accurately extracted from any group.

<Second Embodiment>
Hereinafter, the second embodiment will be described. In the second embodiment, the keyword is extracted based on the number of similar documents in which the keyword candidates appearing in the document to be extracted of the keyword appear among a plurality of similar documents similar to the document to be extracted by the keyword. It differs from the above-described first embodiment in that keywords are extracted from the target document. Hereinafter, the differences from the first embodiment will be mainly described, and the points common to the first embodiment will be omitted. In the description of the second embodiment, the same parts as those of the first embodiment will be described with the same reference numerals.

FIG. 8 is a diagram showing an example of the configuration of the information processing apparatus 100A according to the second embodiment. As shown in the figure, the information processing apparatus 100A includes, for example, a communication unit 102, a control unit 110A, and a storage unit 130A.

The control unit 110A in the second embodiment includes, for example, the keyword assignment unit 112, the document classification unit 114, the extractor evaluation unit 116, the similar document selection unit 118, and the learning processing unit 120. To prepare for.

In addition to various programs such as firmware and application programs, keyword extractor data 132, and document data 134, the storage unit 130A in the second embodiment further stores similar document data 136.

The similar document data 136 is data including a plurality of documents that can be similar to the document (each document included in the document data 134) to be extracted by the keyword. When documents are "similar" to each other, it means that when each document to be compared is vectorized, the vectors of the documents are closely related to each other in a certain vector space.

[Processing flow]
Hereinafter, the flow of a series of processes by the information processing apparatus 100A in the second embodiment will be described according to a flowchart. FIG. 9 is a flowchart showing a flow of a series of processes by the information processing apparatus 100A in the second embodiment. The processing of this flowchart may be repeated, for example, at a predetermined cycle.

First, the similar document selection unit 118 selects a similar document similar to a target document (hereinafter referred to as a keyword extraction target document) for which a keyword is extracted by the keyword extractor EX among a plurality of documents included in the document data 134. Select from a plurality of documents included in the similar document data 136 (S200). The keyword extraction target document is another example of the “document of interest”.

For example, the similar document selection unit 118 generates a multidimensional vector having statistics such as the frequency of appearance of each word included in the keyword extraction target document as each element as a keyword extraction target document vector obtained by vectorizing the keyword extraction target document. do. In addition, the similar document selection unit 118 uses algorithms such as word2vec and doc2vec to learn the task of predicting words that appear before and after a certain word of interest, or uses other existing methods to obtain keywords. The document vector to be extracted may be generated.

The similar document selection unit 118 derives the degree of similarity between the generated keyword extraction target document vector and the vector of each document (hereinafter referred to as a similar document candidate vector) included in the similar document data 136 that is a candidate for a similar document. do. It is assumed that the similar document candidate vector is generated in advance by using the above-mentioned method for generating the keyword extraction target document vector.

For example, the similar document selection unit 118 derives the cosine similarity between the keyword extraction target document vector and the similar document candidate vector, and among a plurality of similar document candidate vectors, the higher rank with the larger cosine similarity with the keyword extraction target document vector. A predetermined number (for example, 10) of similar document candidate vectors are extracted, and all similar document candidate vectors whose cosine similarity with the keyword extraction target document vector is equal to or greater than the threshold value are extracted. Then, the similar document selection unit 118 selects the document that is the source of the extracted similar document candidate vector as the similar document.

Next, the keyword assigning unit 112 causes the keyword extractor EX to count the number of similar documents in which a word X appearing in the keyword extraction target document appears, and TF-IDF is generated based on the counted number. The calculation is performed, and weights are given to the candidate words and phrases of each keyword included in the keyword extraction target document (S202).

The keyword extractor EX in the second embodiment calculates TF-IDF for each keyword extraction target document based on, for example, the mathematical formula (4).

The keyword extractor EX obtains the ratio of the number of similar documents in which the word X appearing in the keyword extraction target document appears by the number of all similar documents among a plurality of similar documents, and further determines the ratio. Of all the documents included in the similar document data 136 regardless of the document, the TF-IDF for the word X is derived by dividing by the logarithm of the number of documents in which the word X appearing in the keyword extraction target document appears. The keyword extractor EX derives TF-IDF for each keyword candidate included in the keyword extraction target document while changing the word X. By such processing, the weight of the word X that is likely to appear in both the document to which the keyword is to be given and the similar document similar to the document and is difficult to appear in other documents can be increased.

The general TF-IDF is such that the word X, which is likely to appear in a document to which a keyword is to be added and is unlikely to appear in other documents including similar documents, has a larger weight. Therefore, it is easy to extract words and phrases peculiar to the keyword extraction target document as keywords. The peculiar words and phrases are, for example, expressions of peculiar phrases in the document and coined words created by the document creator. Since the IDF of such a unique word or phrase is larger than that of other words or phrases, it is easy to be extracted as a keyword. If this keyword is used for document search, it is similar to the document from which the keyword is extracted. It can be difficult to search for documents that have been created.

On the other hand, in the present embodiment, the molecular formula of TF-IDF is determined by how many times the word appears in the own document, and therefore, among a plurality of similar documents, how many similar documents include the word in the own document. By substituting whether or not is included, it is possible to extract keywords that are more shared between documents.

Next, the keyword assigning unit 112 causes the keyword extractor EX to extract one or more keywords from each document included in the document data 134 based on the calculated TF-IDF, and uses the keywords as the extraction source document. Grant (S204).

Since the subsequent processes from S206 to S216 are the same as the processes from S102 to S112 described above, the description thereof will be omitted.

Next, the learning processing unit 120 learns (determines) the hyperparameters of the keyword extractor EX based on the evaluation result of the keyword extractor EX by the extractor evaluation unit 116 (S218). For example, the learning processing unit 120 derives the number of similar documents (predetermined number described above) to be referred to when calculating TF-IDF and the degree of similarity between vectors so that the F value of the keyword extractor EX becomes large. Determine hyperparameters such as method, length of keywords to be extracted, and part of speech of keywords.

Further, the learning processing unit 120 may learn the keyword extractor EX so that the keywords obtained by the above-mentioned method can be extracted even if a similar document is not given. More specifically, the learning processing unit 120 supervisedly learns the keyword extractor EX using the keywords obtained in the processing of S204 as correct answer data. As a result, it is possible to accurately extract keywords that are highly shared between documents without preparing similar documents in advance.

In the above description, the keyword extractor EX has been described as counting the number of times that the word X appearing in the keyword extraction target document also appears in a similar document, but the present invention is not limited to this. For example, in the keyword extractor EX, the contribution of the molecular formula of TF-IDF may be increased as the similar documents are more similar to the keyword extraction target document (similar documents having a higher degree of similarity). For example, when documents X, Y, and Z exist as similar documents, TF-IDF may be obtained based on the mathematical formula (5).

In the formula, W _X represents the similarity of the document X, W _Y represents the similarity of the document Y, and W _Z represents the similarity of the document Z. The keyword extractor EX extracts keywords with higher commonality between documents by using the average of each similarity of similar documents in which the word X appearing in the keyword extraction target document appears as the numerator of TF-IDF. be able to.

Further, when similar documents are ranked according to the degree of similarity with the keyword extraction target document, the keyword extractor EX may be weighted according to the magnitude of the rank. For example, the keyword extractor EX assigns a weight of 1.0 to a similar document having the highest rank, which is most similar to the document to be extracted by the keyword, and a similar document having the second rank, which is similar to the document to be extracted by the keyword. May be given a weight of 0.9, and a weight of 0.8 may be given to a similar document having a rank of 3 similar to the document to be extracted by the keyword. This makes it possible to extract keywords that are more shared between documents.

[Usage scene]
FIG. 10 is a diagram showing an example of a usage scene of a keyword extracted by the keyword extractor EX. In the illustrated example, one page of a shopping site is schematically represented. In the area indicated by R1 in the figure, an introductory text of the product is posted. Such an introductory text is treated as a document for which keywords are extracted. For example, the introductory text includes the model number of the product (“ABCDEF-24” in the example in the figure), but in the case of a general TF-IDF that does not consider the number of occurrences of words in similar documents, the model number is used. The weight of the expressed word or phrase becomes large, and the model number is easily extracted as a keyword. However, when searching for a product similar to that product, words or phrases that have a conceptually higher meaning than the model number are suitable as keywords. Words and phrases that have a higher conceptual meaning are those that easily co-occur with words and phrases contained in other product introductions, and in the illustrated example, words such as "LCD TV" are applicable.

In this embodiment, since the TF-IDF is obtained based on the number of times (ratio) that the word X appearing in the keyword extraction target document also appears in similar documents, a word or phrase (general purpose) peculiar to the page such as a model number is obtained. It becomes difficult to extract untargeted words and phrases), and keywords that are highly shared among shopping sites can be extracted. As a result, for example, when the extracted keyword is a word representing a product category, it is not necessary to comprehensively prepare the product categories in which the products can be classified. For example, each time a product is added to a shopping site, a keyword is extracted from the introductory text of the web page on which the product is posted, and if the extracted keyword is a word or phrase that represents an existing product category, a new addition is added. If the extracted product is not a word or phrase that represents an existing product category, a new product category is created based on that keyword, and a newly added product is created. It is possible to perform operations such as classifying into the product categories that have been selected.

According to the second embodiment described above, among a plurality of similar documents similar to the document to be extracted by the keyword, the number of similar documents in which the candidate of the keyword appearing in the document to be extracted by the keyword appears is based on the number of similar documents. Since the keywords are extracted from the documents to be extracted, it is possible to extract the keywords that are more shared among the documents. As a result, when a user searches for a document, by using keywords that are highly shared between the documents, it is possible to easily search for related documents related to the document, and the document searched by the user is more related. And more relevant documents can be provided. As a result, the efficiency of user information collection can be further improved.

<Hardware configuration>
The information processing apparatus 100 of the above-described embodiment is realized by, for example, a hardware configuration as shown in FIG. FIG. 11 is a diagram showing an example of the hardware configuration of the information processing devices 100 and 100A of the embodiment.

Information processing devices 100 and 100A include NIC100-1, CPU100-2, RAM100-3, ROM100-4, secondary storage devices 100-5 such as flash memory and HDD, and drive devices 100-6 as internal buses or dedicated devices. It is configured to be connected to each other by a communication line. A portable storage medium such as an optical disk is mounted on the drive device 100-6. A program stored in a portable storage medium mounted on the secondary storage device 100-5 or the drive device 100-6 is expanded in the RAM 100-3 by a DMA controller (not shown) or the like, and executed by the CPU 100-2. As a result, the control unit 110 or 110A is realized. The program referenced by the control unit 110 or 110A may be downloaded from another device via the network NW.

Although the embodiments for carrying out the present invention have been described above using the embodiments, the present invention is not limited to these embodiments, and various modifications and substitutions are made without departing from the gist of the present invention. Can be added.

1 ... Information processing system, 10 ... Terminal device, 20 ... Service providing device, 100, 100A ... Information processing device, 102 ... Communication unit, 110, 110A ... Control unit, 112 ... Keyword assigning unit, 114 ... Document classification unit, 116 ... Extractor evaluation unit, 118 ... Similar document selection unit, 120 ... Learning processing unit, 130, 130A ... Storage unit

Claims (9)

A processing unit that extracts the keyword candidates included in the focus document as keywords based on the number of similar documents in which the keyword candidates appearing in the focus document appear among a plurality of similar documents similar to the focus document. Equipped with
Keyword extractor. The processing unit
A weighting coefficient is calculated for each keyword candidate based on the number of similar documents in which keyword candidates appearing in the document of interest appear.
Based on the calculated weighting coefficient, the keyword is extracted from a plurality of candidates for the keyword included in the document of interest.
The keyword extraction device according to claim 1. The processing unit
The ratio of the number of similar documents in which keyword candidates appearing in the document of interest are divided by the number of the plurality of similar documents is calculated.
The ratio calculated by the logarithm of the number of documents in which the keyword candidates appearing in the document of interest appear among all the documents including the plurality of similar documents and the plurality of dissimilar documents not similar to the document of interest. Divide and
The quotient of the ratio and the logarithmic value is calculated as the weighting factor.
The keyword extraction device according to claim 2. The processing unit repeatedly calculates the weighting coefficient for each keyword candidate included in the focused document while changing the keyword candidates appearing in the focused document.
The keyword extraction device according to claim 2 or 3. The processing unit
A weighting coefficient is calculated for each candidate of the keyword based on the degree of similarity of the similar document to the document of interest in which the candidate of the keyword appearing in the document of interest appears.
Based on the calculated weighting coefficient, the keyword is extracted from a plurality of candidates for the keyword included in the document of interest.
The keyword extraction device according to any one of claims 1 to 4. The processing unit
The average of the similarity of each of the plurality of similar documents was calculated.
The logarithmic value of the number of documents in which the keyword candidates appearing in the document of interest appear among all the documents including the plurality of similar documents and the plurality of dissimilar documents not similar to the document of interest, and the degree of similarity. Divide the average and
The quotient between the average of the similarity and the logarithmic value is calculated as the weighting factor.
The keyword extraction device according to claim 5. The processing unit increases the weighting coefficient as the similarity is larger, and decreases the weighting coefficient as the similarity is smaller.
The keyword extraction device according to claim 5 or 6. The computer
Among a plurality of similar documents similar to the document of interest, the candidate of the keyword included in the document of interest is extracted as a keyword based on the number of the similar documents in which the candidate of the keyword appearing in the document of interest appears.
Keyword extraction method. On the computer
Extracting the keyword candidates included in the focus document as keywords based on the number of the similar documents in which the keyword candidates appearing in the focus document appear among a plurality of similar documents similar to the focus document.
A program to execute.

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