JP2019125317A - Device, method, and program for processing information - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an information processing device, an information processing method, and an information processing program capable of realizing appropriate support to a user who answers a question. In an information processing system, an information processing server includes a first acquisition unit that acquires a question sentence, and a relation indicating a relation between the question sentence and each of a plurality of information services based on attribute information regarding attributes of the question sentence. Generating unit for generating degree information, an extracting unit for extracting a predetermined text from a question sentence, a selecting unit for selecting a predetermined information service from a plurality of information services based on related degree information, and a predetermined information service A second acquisition unit that acquires related information related to a predetermined text. [Selection diagram] Fig. 5

Description

  The present invention relates to an information processing apparatus, an information processing method, and an information processing program.

  In recent years, information sharing services between users using the Internet have become popular. In such a service, with respect to a question sentence posted by a user (questioner), another user (answerer) posts an answer sentence, thereby sharing knowledge and wisdom among the users.

Japanese Patent Application Publication No. 06-274539

  It is desirable to have a large number of users answer questions in order to ensure that services are actively used. However, in order to write an answer sentence, it is necessary to have deep knowledge and detailed knowledge beyond simply knowing the answer. Some users leave the service without writing an answer sentence, even though they may be able to answer.

  The present application is made in view of the above, and it is an object of the present invention to realize appropriate support for a user who answers a question.

  The information processing apparatus according to the present application generates a first acquisition unit that acquires a question sentence, and generates association degree information indicating a relation between the question sentence and each of a plurality of information services based on attribute information on an attribute of the question sentence. And a unit.

  According to an aspect of the embodiment, it is possible to realize appropriate support for the user who answers the question.

FIG. 1 is a diagram illustrating an operation of the information processing system according to the embodiment. FIG. 2 is a diagram showing the flow of processing of the information processing server. FIG. 3 is a diagram showing an example of the score information. FIG. 4 is a diagram showing how the information processing server extracts an important word and an important sentence from a question sentence. FIG. 5 is a diagram illustrating an exemplary configuration of the information processing server according to the embodiment. FIG. 6 is a diagram showing an example of information registered in the question information database. FIG. 7 is a diagram showing an example of information registered in the model database. FIG. 8 is a diagram illustrating an exemplary configuration of a terminal device according to the embodiment. FIG. 9 is a flowchart illustrating an example of the information transmission process. FIG. 10 is a hardware configuration diagram showing an example of a computer for realizing the function of the information processing server.

  Hereinafter, an information processing apparatus, an information processing method, and a mode for carrying out an information processing program according to the present application (hereinafter, referred to as “embodiment”) will be described in detail with reference to the drawings. Note that the information processing apparatus, the information processing method, and the information processing program according to the present application are not limited by the following embodiments. Moreover, the same code | symbol is attached | subjected to the same site | part in the following each embodiment, and the overlapping description is abbreviate | omitted.

[1. Operation of Information Processing Device]
First, the operation of the information processing apparatus will be described using the information processing system 1 including the information processing server 10 as an example of the information processing apparatus as an example.

[1-1. Information processing system]
FIG. 1 is a diagram showing an operation of the information processing system 1 according to the embodiment. The information processing system 1 includes an information processing server 10, the service providing server ₂₀ 1 to 20 _3, the terminal device ₁₀₀ 1 to 100 _2. Although only three service providing servers are shown in the example of FIG. 1, the number of service providing servers may be less than three or more than three. In the following description, it is possible to collectively service providing server 20 ₁ to 20 ₃ and the like is described as the service providing server 20. Also, although only two terminal devices are shown in the example of FIG. 1, the number of terminal devices may be less than two or more than two. In the following description, it is possible to collectively terminal device ₁₀₀ 1 to 100 ₂ and the like is described as the terminal device 100.

  The information processing server 10 is a server host computer that provides various services to client terminals. The information processing server 10 distributes content to the terminal device 100. For example, the information processing server 10 may be a portal site, a game information distribution site, a news site, an auction site, a weather forecast site, a shopping site, a finance (stock price) site, a route search site, a map providing site, a travel site, a restaurant introduction site The terminal device 100 is distributed with content in which information related to a bulletin board site, a web blog and the like is arranged in a tiled manner. The information processing server 10 of the present embodiment provides an information sharing service that allows users to ask questions and answers.

The service providing server 20 is a server host computer that provides various services to client terminals. For example, the service providing server 20 transmits information such as information search service, SNS (Social Networking Service), news distribution service, information summarizing service, Internet encyclopedia service, Internet dictionary service, blog service (hereinafter referred to as information service) It is a server that provides ..). The service providing server 20 _1, service Y1 (e.g., news distribution service) which is one of the information service is a server that provides. The service providing server 20 _2, service Y2 (e.g., online encyclopedia service) which is one of the information service is a server that provides. The service providing server 20 _3, service Y3 (e.g., information retrieval service), which is one of the information service is a server that provides.

  The terminal device 100 is a smart device such as a smartphone or a tablet, and is a portable terminal device that can communicate with any server device via a wireless communication network such as 3G (3rd Generation) or LTE (Long Term Evolution). is there. The terminal device 100 may be an information processing device such as a desktop PC (Personal Computer) or a notebook PC as well as a smart device.

[1-2. Information Sharing Service]
As described above, the information processing server 10 provides an information sharing service. In the information sharing service, other users (answerers) post an answer sentence with respect to a question sentence posted by a user (querier), thereby sharing knowledge and wisdom among the users. The information processing server 10, in response to a request from the terminal device 100, content for posting a question sentence (hereinafter referred to as question posting content) or content for posting an answer sentence to a question (hereinafter, answer posting) ) Is distributed to the terminal device 100. The terminal device 100 receives the distribution of the content and displays the question posting content and the answer posting content on the screen. In the example of FIG. 1, are displayed the content C1 is Contributions contents to the terminal apparatus 100 ₁ of the user U1, the content C2 is displayed a reply content posted to the terminal apparatus 100 ₂ user U2. The contents C1 and C2 are, for example, screens of web pages and applications. In the example of FIG. 1, the user U1 is a questioner and the user U2 is a respondent.

  In the content C1, a text box TB1 for inputting a question sentence is arranged. When the user U1 inputs a question sentence in the text box TB1 and presses a post button, the question sentence is transmitted to the information processing server 10. Further, in the content C2, a text box TB2 for inputting an answer sentence is arranged. When the user U2 inputs an answer sentence in the text box TB2 and presses a post button, the answer sentence is transmitted to the information processing server 10.

[1-3. Operation of information processing system]
Hereinafter, the operation of the information processing system 1 will be described.

First, the information processing server 10 receives a distribution request for question posted content from the terminal device 100 (the terminal device 100 1 in the example of FIG. ₁ ). When receiving the request for distributing the question posted content, the information processing server 10 distributes the content C1 which is the question posted content to the terminal device 100 that has made the request for distributing the question content. When the user inputs a question sentence into the text box TB1 and presses the post button, the terminal device 100 transmits the question sentence to the information processing server 10 (step S1). When acquiring the question sentence from the terminal device 100, the information processing server 10 registers the question sentence in the database together with the information (for example, the user ID) of the questioner. Multiple question sentences are registered in the database.

In addition, the information processing server 10 receives a distribution request for answer posted content from the terminal device 100 (the terminal device 100 _{2 in} the example of FIG. 1) (step S2). When the information processing server 10 receives the distribution request of the answer post content, the information processing server 10 acquires a question sentence to be distributed from the database.

  Then, the information processing server 10 acquires attribute information of the question sentence acquired in step S2 (step S3). Attribute information is information on an attribute of a question sentence. The attribute of the question sentence is, for example, the characteristic or the property of the question sentence, such as whether the content of the question sentence is academic, everyday, or current event. In the present embodiment, the information processing server 10 acquires, as attribute information, strength information (first strength information) indicating the strength of the association between the question sentence and each of the plurality of attributes. At this time, the strength information may be numerical values or may be ranks such as A, B, C, and the like.

  FIG. 2 is a diagram showing the flow of processing of the information processing server 10. In the example of FIG. 2, a plurality of classes are prepared for each attribute of the question sentence. Classes X 1, X 2, X 3, etc. shown in FIG. 2 are classes in which a question sentence is classified for each attribute. The class X1 is, for example, a class indicating that the contents of the question sentence are current events. The class X2 is, for example, a class indicating that the content of the question sentence is routine. The class X3 is, for example, a class indicating that the content of the question sentence is academic. The numbers attached to the side of each class are strength information indicating the strength of the association between the question sentence and the attribute. In the example of FIG. 2, the question text is "Hello, I am a high school student, but it is a question. In the case of this question sentence, the strength of the relation between the question sentence and the class X1 is 0.9, the strength of the relation between the question sentence and the class X2 is 0.3, and the strength of the relation between the question sentence and the class X3 is 0 .1. In the example of FIG. 2, it can be seen that the question sentence is strongly associated with the class X1 (for example, the attribute indicating the current event material).

  The information processing server 10 may generate such attribute information using the model M1. The model M1 is a trained model that is trained to output attribute information of the question text when the question text is input, using data of a set of the first text and information indicating the attribute of the first text as learning data. is there. Here, the “first text” is, for example, an information sharing service provided by the operator who operates the information processing server 10, and is a question sentence of a question that the user has made in the past. In addition, “information indicating the attribute of the first text” is, for example, an attribute determined by the operator (or the employee of the operator) looking at the first text (whether the content of the first text is academic or not, Information on daily life, current events, etc.). As shown in FIG. 2, the information processing server 10 generates attribute information by inputting a question sentence to the model M1. Note that it is not necessary to use a model to generate attribute information of a question sentence. The information processing server 10 may determine the attribute of the question sentence using various other methods.

  Next, the information processing server 10 generates association degree information indicating the association between the question sentence and each of the plurality of information services based on the attribute information (step S4). In the present embodiment, the information processing server 10 acquires, as the degree of association information, intensity information indicating the degree of association between the question sentence and each of the plurality of information services. At this time, the strength information may be numerical values or may be ranks such as A, B, C, and the like.

  In the present embodiment, a plurality of information services are registered in the information processing server 10. Services Y1, Y2, Y3, Y4, etc. shown in FIG. 2 are information services. The service Y1 is, for example, a news distribution service. The service Y2 is, for example, an Internet encyclopedia service. The service Y3 is, for example, an information search service. The service Y4 is, for example, a blog service. The number attached to the side of each service is strength information indicating the strength of the relation between the question sentence and the information service. In the case of the question sentence shown in FIG. 2, the strength of the relation between the question sentence and the service Y1 is 1.1, the strength of the relation between the question sentence and the service Y2 is 0.7, and the relation between the question sentence and the service Y3 The strength of the question mark is 0.1, and the relation strength between the question sentence and the service Y4 is -0.1. In the example of FIG. 2, it can be seen that the question sentence has a strong relationship with the service Y1 (for example, the news distribution service).

  The information processing server 10 may generate such degree of association information using the score information. The score information is information in which a score (second strength information) indicating the strength of the association between each of the plurality of attributes and each of the plurality of information services is recorded. The score information may be generated by a person judging the strength of the relation between the question sentence attribute and the information service one by one or learning the strength of the relation between the question sentence attribute and the information service It may be generated using a learning model.

  FIG. 3 is a diagram showing an example of the score information. Information having items such as “class”, “service”, and “score” is registered in the score information. The “class” is information for identifying a plurality of attributes of the question sentence. The “service” is information for identifying a plurality of information services registered in the information processing server 10. “Score” is strength information indicating the strength of association between the attribute specified in the item of class and the information service specified in the item of service. The score is, for example, a numerical value in the range of 0.0 to 1.0. Of course, the numerical range of the score may be other than this.

  In the example shown in FIG. 3, information such as class “X1”, service “Y1”, and score “Z11” is registered in association with each other. Such information indicates that, for example, the score indicating the strength of the association between the attribute indicated by the class “X1” and the information service indicated by the service “Y1” is “Z11”. In the example shown in FIG. 3, conceptual information such as “X11 to X3”, “Y1 to Y4”, and “Z11 to Z34” is shown as the information registered in the score information, but in actuality, it is a text Data and binary data will be registered.

  The information processing server 10 generates association degree information based on, for example, the attribute information acquired in step S3 and the score information. More specifically, the information processing server 10 is based on the strength information (first strength information) recorded in the attribute information and the score (second strength information) recorded in the score information. Generate information.

  For example, it is assumed that the information processing server 10 calculates the strength of the association between the question sentence shown in FIG. 2 and the service Y1. In this case, the information processing server 10 first obtains, from the attribute information, information on the strength of the association between the question sentence and each class indicating the attribute of the question sentence. As described above, in the example of FIG. 2, the question sentence and class X1 and the strength of association are 0.9, the question sentence and class X2 and the strength of association are 0.3, and the question sentence and the class X3 The strength of the association is 0.1. Furthermore, the information processing server 10 acquires a score indicating the strength of the association between the service Y1 and each class. As shown in FIG. 3, the score indicating the strength of association between class X1 and service Y1 is Z11, the score indicating the strength of association between class X2 and service Y1 is Z21, and the score between class X3 and service Y1 is The score indicating the strength of association is Z31. The information processing server 10 associates, for example, a value obtained by multiplying the question sentence and the class X1 and the relation strength 0.9 with the class X1 and the service Y1 and the relation strength between the question sentence and the class X2. Of the strength of the relation between the class X2 and the service Y1 and Z21 which is the strength of the relation between the class X2 and the service Y1, 0.1, the class X3 and the class X3 and the service Y1 A value obtained by adding a value obtained by multiplying Z31, which is the strength of the relation, is calculated as strength information indicating the strength of the relation between the question sentence and the service Y1. The information processing server 10 executes this process for all information processing services. This generates relevance information. The above is only an example. The information processing server 10 can calculate the degree of association information using various methods.

  Next, the information processing server 10 selects a predetermined information service from among a plurality of information services based on the degree of association information (step S5). For example, the information processing server 10 selects one or more information services from among a plurality of information services in descending order of the degree of association with the question sentence based on the degree of association information. For example, the information processing server 10 selects an information service whose value indicating the strength of the association between the question sentence and the information service is equal to or more than a predetermined threshold value. In the example of FIG. 2, the information processing server 10 selects three services Y1, Y2 and Y3 whose values indicating the strength of the association between the question sentence and the information service are 0 or more. The information processing server 10 stores information of the selected information service (hereinafter, referred to as selection information) in the storage unit.

  Next, the information processing server 10 extracts a predetermined text from the question sentence (step S6). At this time, the information processing server 10 may extract a predetermined word (word) or a predetermined sentence (sentence) included in the question sentence as a predetermined text. The predetermined word is a word included in the question sentence, for example, an important word included in the question sentence. Further, the predetermined sentence is a sentence included in the question sentence, for example, an important word included in the question sentence. For example, it is assumed that the question to be processed is "Hello, I am a high school student, but I have a question. At this time, the information processing server 10 extracts, for example, “Mr. H” and “Mr. F” as predetermined words (important words). In addition, the information processing server 10 extracts, for example, “Wh-H? W-W? W?” As a predetermined sentence (important sentence).

  The information processing server 10 may extract the key words and the key sentences using the model M2. FIG. 4 is a diagram showing how the information processing server 10 extracts an important word and an important sentence from a question sentence. The model M2 is a model generated by learning based on the first text and the second text that is at least one of an important word and an important sentence included in the first text. For example, in the model M2, when the first text (question sentence) is input, the second text (important terms and / or important sentences included in the question text) included in the first text (question sentence) Is a trained model that has been trained to output Here, the “first text” is, for example, a question sentence of a question that the user has made in the past in the information sharing service provided by the operator. Also, the “second text” is, for example, a key word and / or a key sentence in the first text determined by the operator (or the operator of the operator) looking at the first text. The information processing server 10 extracts a predetermined word (important word) or a predetermined sentence (important sentence) included in the question sentence by inputting the question sentence to the model M2, as shown in FIG. Note that it is not necessary to use a model to extract key words and key sentences. The information processing server 10 can extract the key words and the key sentences using various other methods.

  The information processing server 10 acquires information (hereinafter referred to as related information) related to the predetermined text (important word and / or important sentence) extracted in step S6 from the information service selected in step S5 (step S7a, S7b, S7c). For example, it is assumed that “棋 H” and “棋 F” are extracted as key words in step S6. At this time, the information processing server 10 uses the information service (for example, the information search service) selected in step S5 to acquire information related to the important word. For example, the information processing server 10 acquires, as related information, a result of searching for an important word as a search query. In addition, it is assumed that "Who is H. W or W? F? Is more important?" Is extracted as an important sentence in step S6. At this time, the information processing server 10 uses the information service (for example, SNS) selected in step S5 to acquire information on important sentences. For example, the information processing server 10 acquires a sentence similar to the important sentence from the SNS.

  If a plurality of information services are selected in step S5, the information processing server 10 may acquire related information from each of the plurality of information services. At this time, the information processing server 10 may be configured such that the amount of information of the related information to be acquired increases as the information service has a higher degree of association with the question sentence.

Next, the information processing server 10 generates the content C2 to be transmitted to the terminal device 100. The content C2 is content relating to a question sentence and includes information relating to related information. More specifically, the content C2 is content for answering a question sentence (answer post content), and is content including a related information or a link for displaying the related information. In the example of FIG. 1, the content being displayed on the terminal device 100 ₂ is a content C2. In the content C2, in addition to a text box TB2 for inputting an answer sentence, related information R1 and R2 are displayed.

  The information processing server 10 transmits the generated content C2 to the terminal device 100 (step S8). When the user inputs an answer sentence in the text box TB2 and presses a post button, the terminal device 100 transmits an answer sentence to the information processing server 10 (step S9).

  According to the present embodiment, the information processing server 10 is configured to generate association degree information indicating the association between the question sentence and each of the plurality of information services based on the attribute information on the attribute of the question sentence. Therefore, since the information processing server 10 can know which information service can obtain appropriate information by acquiring information related to the question, it is possible to realize appropriate support for the user who answers the question.

[2. Configuration Example of Information Processing System]
The operation of the information processing system 1 of the present embodiment has been described above. The configuration of the information processing system 1 including the information processing server 10, which is an example of the information processing apparatus of the present embodiment, will be described below. As described above, the information processing system 1 includes the terminal device 100, the information processing server 10, and the service providing server 20. The service providing server 20 is a server host computer that provides various services to client terminals (including the information processing server 10).

[2-1. Configuration Example of Information Processing Server]
The information processing server 10 is a server host computer (hereinafter simply referred to as a “server”) that processes requests from client computers such as the terminal device 100. The information processing server 10 may be a PC server, a midrange server, or a mainframe server. Further, the information processing server 10 may be configured by one server, or may be configured by a plurality of servers that cooperate to execute processing. When the information processing server 10 is configured by a plurality of servers, the installation locations of these servers may be remote. These servers can be regarded as one information processing apparatus (information processing server) if the processing is performed in cooperation with each other even if the installation place is far.

  As described above, the information processing server 10 is connected to the service providing server 20 and the terminal device 100 via the network. The network is a communication network such as a Local Area Network (LAN), a Wide Area Network (WAN), a telephone network (such as a mobile telephone network or a fixed telephone network), a regional IP (Internet Protocol) network, or the Internet. The network may include a wired network or may include a wireless network.

  FIG. 5 is a diagram showing an exemplary configuration of the information processing server 10 according to the embodiment. The information processing server 10 includes a communication unit 11, a storage unit 12, and a control unit 13. The configuration shown in FIG. 5 is a functional configuration, and the hardware configuration may be different therefrom.

  The communication unit 11 is a communication interface that communicates with an external device. The communication unit 11 may be a network interface or a device connection interface. For example, the communication unit 11 may be a LAN interface such as a NIC (Network Interface Card), or may be a USB interface configured by a USB (Universal Serial Bus) host controller, a USB port, or the like. The communication unit 11 may be a wired interface or a wireless interface. The communication unit 11 functions as a communication unit of the information processing server 10. The communication unit 11 communicates with the service providing server 20 and the terminal device 100 according to the control of the control unit 13.

  The storage unit 12 is a storage device capable of reading and writing data, such as a dynamic random access memory (DRAM), a static random access memory (SRAM), a flash memory, and a hard disk. The storage unit 12 functions as a storage unit of the information processing server 10. The storage unit 12 stores a question information database 121 and a model database 122.

  Question information is registered in the question information database 121. FIG. 6 is a diagram showing an example of information registered in the question information database 121. As shown in FIG. In the question information database 121, information having items such as “question ID (Identifier)”, “category”, “question sentence”, “questioner information”, and “answer” is registered.

  "Question ID" is an identifier for identifying a question made at a question site. Also, "category" is a category of the question identified by the question ID. For example, "category" is a classification of questions such as music, health, business. Also, “question sentence” is a question sentence of a question identified by the question ID. "Questioner information" is information on the questioner identified by the question ID. For example, “questioner information” is identification information (for example, user ID) of a requester or user attribute information (for example, demographic information or psychographic information). As a specific example, the user attribute information is the age of the user (eg, questioner or respondent), occupation, income, property, address, address of work, number of languages used, family structure, friend relationship, life cycle, value It is information of views, lifestyles, characters, preferences, and these changes. Further, the user attribute information may include a user's search history and service usage history. Of course, the user attribute information is not limited to the above information.

  Also, “Answer” is information on an answer to the question identified by the question ID. Information having items such as "answer text", "responder information", and "evaluation" is registered in the item "answer". “Answer sentence” is an answer sentence to the question identified by the question ID. "Responder information" is information of a respondent who posted an answer sentence. For example, “responder information” is identification information (for example, user ID) or attribute information (for example, demographic information or psychographic information) of the respondent. Also, "evaluation" is information on the evaluation made by the respondent. For example, the evaluation is a score indicating whether or not the best answer has been obtained, and the quality of the answer (for example, the number of stars indicating the evaluation, the number of "likes", etc.). In the example of FIG. 6, only one item “answer” is provided for one question, but multiple pieces of information “answer” may be provided for one question.

  For example, in the example shown in FIG. 6, the question ID “Q21”, the category “CT1”, the question sentence “TQ21”, the questioner information “UQ21”, the answer sentence “TA21”, the answerer information “UA21”, and the evaluation “E21 Information such as "is associated and registered. Such information includes, for example, the category indicated by “CT1”, the question sentence indicated by “TQ21”, the questioner information indicated by “UQ21”, and “TA21” in the question information indicated by the question ID “Q21”. It shows that the answer text which shows, the reply person information which shows "UA21", and the evaluation which "E21" shows are included. In the example shown in FIG. 6, “Q21 to Q23”, “CT1 to CT3”, “TQ21 to TQ23”, “UQ21 to UQ23”, and “TA21 to TA23” as information registered in the question information database 121, Although conceptual information such as "UA21 to UA23" and "E21 to E23" is shown, text data and binary data are actually registered.

  Returning to FIG. 5, in the model database 122, data of a model that the information processing server 10 has is registered. FIG. 7 is a diagram showing an example of information registered in the model database 122. As shown in FIG. In the example illustrated in FIG. 7, information such as “model ID” and “model data” is registered in the model database 122.

  Here, “model ID” is information for identifying each model. Further, “model data” is data of a model indicated by the associated “model ID”, and, for example, for nodes in each layer, functions adopted by each node, connection relationship of nodes, and connection between nodes It is information including a connection coefficient to be set.

  For example, in the example illustrated in FIG. 7, information such as a model ID “4001” and model data “M1” is registered in association with each other. Such information indicates, for example, that the data of the model indicated by "4001" is "M1". In the example illustrated in FIG. 7, conceptual information such as “M1 to M3” is described as the information registered in the model database 122. However, actually, a character string or a numerical value indicating the structure of the model or the connection coefficient Etc. will be registered. Hereinafter, M1 to M3 and the like are collectively referred to as a model M.

  The model M is a question when the question text is input using, as training data, data of a pair of the first text (question text) and the second text (key word and / or key text) included in the first text. This is a model learned to output words (keywords) and / or sentences (important sentences) included in sentences. At this time, the second text to be the teacher label may be an important word and / or an important sentence extracted by a human hand from the first text (question sentence). Such a model M includes an input layer for inputting a question sentence, an output layer for outputting a word (important word) and / or a sentence (important sentence) included in the question sentence, and any one from the input layer to the output layer. Input layer that includes a first element belonging to a layer other than the output layer, and a second element whose value is calculated based on the first element and the weight of the first element A question input to the input layer by performing an operation on information based on the first element and the weight of the first element (that is, the connection coefficient) with each element belonging to each layer other than the output layer as the first element It is a model for functioning a computer to output the word (key word) and / or the sentence (important sentence) included in the question sentence from the output layer according to the sentence.

  In addition, the model M outputs the attribute information of the question sentence when the question sentence is input, using data of a set of the first text (question sentence) and the information indicating the attribute of the first text as learning data. It may be a learned model. Such a model M is an input layer for inputting a question sentence, an output layer for outputting attribute information of the question sentence, and any layer from the input layer to the output layer and belongs to a layer other than the output layer Each element that includes the first element, the second element whose value is calculated based on the first element and the weight of the first element, and the information input to the input layer belongs to each layer other than the output layer By performing an operation based on the first element and the weight of the first element (that is, the connection coefficient) with the first element as the first element, the attribute information of the question sentence is output according to the question sentence input to the input layer It is a model for functioning a computer to output from a layer.

  Here, it is assumed that the model M is realized by a regression model represented by “y = a1 * x1 + a2 * x2 +... + Ai * xi”. In this case, the first element included in the model M corresponds to input data (xi) such as x1 and x2. Also, the weight of the first element corresponds to the coefficient ai corresponding to xi. Here, the regression model can be regarded as a simple perceptron having an input layer and an output layer. When each model is regarded as a simple perceptron, the first element can correspond to any node of the input layer, and the second element can be regarded as a node of the output layer.

  Further, it is assumed that the model M is realized by a neural network having one or more intermediate layers, such as DNN (Deep Neural Network). In this case, the first element included in the model M corresponds to any node that the input layer or the middle layer has. Also, the second element corresponds to the next-stage node that is a node to which a value is transmitted from the node corresponding to the first element. Also, the weight of the first element corresponds to the connection coefficient, which is a weight considered for the value transmitted from the node corresponding to the first element to the node corresponding to the second element.

  The information processing server 10 calculates output information using a model having an arbitrary structure, such as the above-described regression model or neural network. Specifically, when the first text (question sentence) is input, the model M is a second text (an important word and / or included in the question sentence) included in the first text (question sentence) The coefficient is set to output the important sentence). For example, the information processing server 10 inputs, to the model M, the second text (the important word and / or the important sentence included in the question sentence) contained in the first text (the question sentence) and the first text. The coefficient is set based on the degree of similarity between the text obtained. The information processing server 10 extracts a word (important word) and / or a sentence (important sentence) included in the question sentence from the question sentence using such a model M.

  Alternatively, the coefficient is set so that the model M outputs the attribute information of the first text when the first text (question sentence) is input. For example, the information processing server 10 sets a coefficient so that the value indicating the attribute of the first text (question sentence) and the value obtained by inputting the first text to the model M approach each other. The information processing server 10 generates attribute information of the question sentence from the question sentence using such a model M.

  In the above example, an example is shown in which model M is a model (hereinafter referred to as model V1) that outputs an important word and / or an important sentence included in the question sentence when a question sentence is input. . Alternatively, in the above example, an example is shown in which the model M is a model (hereinafter referred to as a model V2) that outputs attribute information of a question sentence when a question sentence is input. However, the model M according to the embodiment may be a model generated based on a result obtained by repeating input and output of data to the model V1 or the model V2.

  In addition, when the information processing server 10 performs learning processing or generation processing using GAN (Generative Advertising Networks), the model M may be a model that constitutes a part of GAN.

  Returning to FIG. 5, the control unit 13 is a controller, and is stored in a storage device inside the information processing server 10 by a processor such as a central processing unit (CPU) or a micro processing unit (MPU). Various programs are realized by executing the RAM or the like as a work area. The control unit 13 is a controller, and may be realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  In addition, the control unit 13 performs the information processing according to the model M stored in the storage unit 12 to convert the coefficient (ie, the model M) that the model M has to the data (question sentence) input to the input layer of the model M. Performs an operation based on the coefficients corresponding to various features learned, and outputs text (for example, important words and / or important sentences included in the question sentence) or attribute information of the question sentence from the output layer of the model M.

  As shown in FIG. 5, the control unit 13 includes a question sentence acquisition unit 131, a learning unit 132, a generation unit 133, an extraction unit 134, a selection unit 135, a related information acquisition unit 136, and a transmission unit 137. And. The blocks (question sentence acquisition unit 131 to transmission unit 137) that constitute the control unit 13 are functional blocks that indicate the functions of the control unit 13, respectively. These functional blocks may be software blocks or hardware blocks. For example, the functional blocks described above may be one software module implemented by software (including a microprogram) or one circuit block on a semiconductor chip (die). Of course, each functional block may be one processor or one integrated circuit. The configuration method of the functional block is arbitrary. The control unit 13 may be configured in functional units different from the above-described functional blocks.

  The question sentence acquisition unit 131 acquires a question sentence from the terminal device 100 or the storage unit 12. The question sentence acquisition unit 131 functions as a first acquisition unit of the information processing server 10.

  The learning unit 132 learns the model M, and stores the learned model M in the model database 122. More specifically, when the learning unit 132 inputs a question sentence into the model M, the model M has a predetermined text (for example, a word (important word) and / or a sentence (important sentence) included in the question sentence) Setting the connection coefficient of the model M so as to output. That is, when the learning unit 132 inputs a question sentence to the model M, the learning unit 132 learns the model M so that the model M outputs a word (important word) and / or a sentence (important sentence) included in the question sentence. I do. Alternatively, the learning unit 132 sets the connection coefficient of the model M so that the model M outputs the attribute information of the question sentence when the question sentence is input.

  For example, the learning unit 132 inputs a first text (a question sentence) to a node of an input layer of the model M, and propagates data to an output layer of the model M by tracing each intermediate layer, thereby obtaining a predetermined text. (For example, the words (key words) and / or the sentences (important sentences) included in the question sentence are output. Then, the learning unit 132 corrects the connection coefficient of the model M based on the difference between the text actually output by the model M and the second text (important word and / or important sentence) included in the learning data. . For example, the learning unit 132 may correct the connection coefficient using a method such as back propagation. At this time, the learning unit 132 corrects the connection coefficient based on the cosine similarity between the vector that is the dispersive expression of the second text and the vector that is the dispersive expression of the text actually output by the model M. It is also good.

  In addition, the learning unit 132 is a node of the input layer of the model M, and the first text (question sentence) of the node corresponding to the input layer of the encoder which learned the feature corresponding to the information input to the input layer. Is input, and each intermediate layer is traced to propagate data to the output layer of the model M, thereby outputting the attribute information of the first text. Then, the learning unit 132 corrects the connection coefficient of the model M based on the difference between the value actually output by the model M and the value indicating the attribute of the first text included in the learning data. For example, the learning unit 132 may correct the connection coefficient using a method such as back propagation.

  The learning unit 132 may learn the model M using any learning algorithm. For example, the learning unit 132 may learn the model M using a learning algorithm such as a neural network, a support vector machine, clustering, reinforcement learning and the like.

  The generation unit 133 generates association degree information indicating the association between the question sentence and each of the plurality of information services based on the attribute information on the attribute of the question sentence. For example, the generation unit 133 acquires, as attribute information, first strength information indicating the strength of the association between the question sentence and each of the plurality of attributes. Furthermore, the generation unit 133 acquires second strength information indicating the strength of the association between each of the plurality of attributes and each of the plurality of information services. Then, the generation unit 133 generates association degree information based on the first intensity information and the second intensity information.

  The extraction unit 134 extracts a predetermined text from the question sentence. For example, the extraction unit 134 extracts a predetermined word or a predetermined sentence included in the question sentence as a predetermined text. At this time, the extraction unit 134 may extract a word or a sentence included in the question sentence from the question sentence using the model M1. The model M1 may be a model generated by learning based on the first text and the second text that is at least one of an important word and an important sentence included in the first text.

  The selection unit 135 selects a predetermined information service from among a plurality of information services based on the degree of association information. For example, the selection unit 135 selects a predetermined number of information services from among a plurality of information services based on the degree of association information in the descending order of the degree of association with the question sentence.

  The related information acquisition unit 136 acquires related information related to a predetermined text from a predetermined information service. For example, the related information acquisition unit 136 acquires related information from each of a predetermined number of information services. The related information acquisition unit 136 functions as a second acquisition unit of the information processing server 10.

  The transmitting unit 137 transmits content that is content related to a question sentence and includes information related to related information. For example, the transmitting unit 137 transmits content that is content for answering a question sentence and includes related information or a link for displaying related information.

[2-2. Configuration Example of Terminal Device]
Next, the configuration of the terminal device 100 will be described. The terminal device 100 is an information display device that a user uses to view content. The terminal device 100 is, for example, a communication terminal such as a smartphone, a tablet, or a mobile phone. As long as it has a communication function, the terminal device 100 may be an information processing terminal such as a personal computer (notebook PC, desktop PC), PDA (Personal Digital Assistant) or the like. In this case, the information processing terminal is also a type of communication terminal. The terminal device 100 connects to the information processing server 10 via a network.

  FIG. 8 is a diagram illustrating an exemplary configuration of the terminal device 100 according to the embodiment. The terminal device 100 includes a communication unit 110, an input unit 120, a storage unit 130, an output unit 140, and a control unit 150. The configuration shown in FIG. 8 is a functional configuration, and the hardware configuration may be different therefrom.

  The communication unit 110 is a communication interface that communicates with an external device. The communication unit 110 may be a network interface or a device connection interface. For example, the communication unit 110 may be a LAN interface such as a NIC, or may be a USB interface configured by a USB host controller, a USB port, or the like. The communication unit 110 may be a wired interface or a wireless interface. The communication unit 110 functions as a communication unit of the terminal device 100. The communication unit 110 communicates with the information processing server 10 according to the control of the control unit 150.

  The input unit 120 is an input device that receives various inputs from the outside. For example, the input unit 120 is an operating device for the user to perform various operations, such as a keyboard, a mouse, and an operation key. The input unit 120 functions as an input unit of the terminal device 100. When a touch panel is adopted for the terminal device 100, the touch panel is also included in the input unit 120. In this case, the user performs various operations by touching the screen with a finger or a stylus.

  The storage unit 130 is a storage device capable of reading and writing data, such as a dynamic random access memory (DRAM), a static random access memory (SRAM), a flash memory, and a hard disk. The storage unit 130 functions as a storage unit of the terminal device 100. The storage unit 130 stores application data. The application data may be web browser data.

  The output unit 140 is a device that performs various outputs such as sound, light, vibration, and an image to the outside. The output unit 140 functions as an output unit of the terminal device 100. The output unit 140 includes a display device that displays various information. The display device is, for example, a liquid crystal display or an organic EL (Electro Luminescence) display. When a touch panel is adopted for the terminal device 100, the display device (hereinafter, referred to as a screen) may be integral with the input unit 120. The output unit 140 displays an image on the screen according to the control of the control unit 150.

  The control unit 150 is a controller, and is implemented, for example, by a processor such as a CPU or an MPU executing various programs stored in a storage device inside the terminal device 100 using a RAM or the like as a work area. The control unit 150 is a controller, and may be realized by an integrated circuit such as an ASIC or an FPGA.

  As shown in FIG. 8, the control unit 150 includes a display unit 151 and a display control unit 152. The blocks (the display unit 151 and the display control unit 152) that constitute the control unit 150 are functional blocks that indicate the functions of the control unit 150, respectively. These functional blocks may be software blocks or hardware blocks. For example, the functional blocks described above may be one software module implemented by software (including a microprogram) or one circuit block on a semiconductor chip (die). Of course, each functional block may be one processor or one integrated circuit. The configuration method of the functional block is arbitrary. The control unit 150 may be configured in function units different from the above-described functional blocks.

  The display unit 151 displays the content C1 or the content C2 transmitted from the information processing server 10 on the screen.

  The display control unit 152 changes the mode of the content C1 or C2 displayed on the screen according to the control information transmitted from the information processing server 10.

[3. Processing Flow of Information Processing Server]
Next, the procedure of the process performed by the information processing server 10 will be described. FIG. 9 is a flowchart illustrating an example of the information transmission process.

  First, the information processing server 10 determines whether a distribution request for the content C2 for answering a question has been received from the terminal device 100 operated by the user (step S11). When the distribution request has not been received (step S11: No), the information processing server 10 repeats step S11 until the distribution request is received.

  When the distribution request is received (step S11: Yes), the information processing server 10 acquires a question sentence from the question information database 121 (step S12). Then, the information processing server 10 generates attribute information of the question sentence (step S13).

  Subsequently, the information processing server 10 generates association degree information indicating the association between the question sentence acquired in step S12 and the information service (step S14). Then, the information processing server 10 selects an information service from among a plurality of information services based on the degree of association information (step S15).

  Subsequently, the information processing server 10 extracts a predetermined text (key word and / or key sentence) included in the question sentence (step S16). Then, the information processing server 10 acquires related information from the information service selected in step S14 (step S17).

  Subsequently, the information processing server 10 generates the content C2 including the related information acquired in step S17 (step S18). The information processing server 10 transmits the content C2 to the terminal device 100 (step S19).

  When the transmission is completed, the information processing server 10 returns to step S11, and stands by until a content distribution request is received from the terminal device 100.

[4. Modified example]
The above embodiment is an example, and various modifications and applications are possible.

  For example, in the above-described embodiment, the information processing server 10 merely places the related information in the content C2. However, the information processing server 10 may change the display order of related information and the number of displayed related information based on the degree of association information. For example, the information processing server 10 may display the related information acquired from the information service having a high degree of association on the upper side of the page. Alternatively, the information processing server 10 may increase the display ratio of the related information acquired from the information service having a high degree of association. For example, if the degree of association between the question sentence and the service Y1 is 1.1 and the degree of association between the question sentence and the service Y2 is 0.7, the information processing server 10 determines the number of related information acquired from the service Y1 and the service Y2 The content C2 is generated such that the ratio to the number of related information acquired from is 11: 7.

  Further, in the above-described embodiment (step S6), the predetermined text extracted from the question sentence is a predetermined word (for example, an important word) included in the question sentence, or a predetermined sentence (for example, Important sentence). However, the information processing server 10 may acquire similar text having a content similar to the extracted text (predetermined word and / or predetermined sentence) extracted from the question sentence as the predetermined text. For example, the information processing server 10 may acquire, as a predetermined text, the similar term of the key word extracted from the question sentence. For example, it is assumed that the question sentence is “Which one is the better?” And “F”, “H” are extracted as the key words. At this time, the information processing server 10 acquires “Mr. K”, which is a word indicating a person having a close relationship with either Mr. F or M or both, as a similar word.

  Note that various methods can be used to generate similar text. For example, the information processing server 10 may generate similar text from the extracted text using a knowledge base created in advance. A knowledge base is a database in which knowledge such as facts, common sense, and experiences can be deciphered by a computer. For example, a knowledge base is an organization of knowledge such as human relationships and human attributes. As an example, the knowledge base has an entity as a node, and indicates a relationship between the entity and the entity, an attribute of the entity, and the like in a relationship graph. Here, an entity is a group of meanings, for example, an entity such as a person, an organization, an organization, a place, a thing, or a thing. If “棋 F” or “棋 H” is extracted as an important word from the question sentence, the information processing server 10 identifies an entity indicating “棋 F” or “棋 H” in the knowledge base, and An entity (for example, a person's name) within a certain distance (a certain degree of relationship) from the corresponding entity is determined as a similar word. Of course, the method of generating similar text is not limited to the above. For example, the information processing server 10 may generate similar text from the extracted text using a learned model generated by learning based on the relationship between the entity and the entity.

  When the generation of the similar text is completed, the information processing server 10 sets the generated similar text as the predetermined text, and the predetermined text (for example, the key word similarity) as described in the above steps S7a, S7b, and S7c. Related information related to the word) is acquired from the information service selected in step S5. As a result, the information processing server 10 can perform more satisfactory support. For example, in the information processing server 10, an answer such as "Your H is strong, or else you were also very strong" was generated in response to the question, "Wh? Support is possible.

  The control device for controlling the information processing server 10 according to the present embodiment may be realized by a dedicated computer system or may be realized by a normal computer system. For example, a program or data (for example, model M) for executing the above-mentioned operation is stored in a computer readable recording medium such as an optical disc, a semiconductor memory, a magnetic tape, a flexible disc and the like and distributed. May be installed in a computer, and the control device may be configured by performing the above-described processing. The control device may be an external device (for example, a personal computer) outside the information processing server 10 or an internal device (for example, the control unit 13). In addition, the program may be stored in a disk device provided in a server device on a network such as the Internet so that the program can be downloaded to a computer. In addition, the above-described functions may be realized by cooperation of an OS (Operating System) and application software. In this case, the part other than the OS may be stored in the medium and distributed, or the part other than the OS may be stored in the server apparatus and downloaded to the computer.

  Further, among the processes described in the above-described embodiments, all or part of the process described as being automatically performed may be manually performed, or the process described as being manually performed. All or part of can be performed automatically by known methods. In addition, information including processing procedures, specific names, various data and parameters shown in the above-mentioned documents and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

  Further, each component of each device illustrated is functionally conceptual, and does not necessarily have to be physically configured as illustrated. That is, the specific form of the distribution and integration of each device is not limited to that shown in the drawings, and all or a part thereof may be functionally or physically dispersed in any unit depending on various loads, usage conditions, etc. It can be integrated and configured.

  Moreover, it is possible to combine suitably each embodiment mentioned above in the range which does not contradict process content.

[5. Hardware configuration]
The information processing server 10 according to the embodiment and the modification can also be realized by, for example, a computer 1000 configured as shown in FIG. FIG. 10 is a hardware configuration diagram showing an example of a computer for realizing the function of the information processing server 10. The computer 1000 includes a central processing unit (CPU) 1100, a RAM 1200, a ROM 1300, a hard disk drive (HDD) 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F). ) 1700.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400 to control each part. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, a program depending on the hardware of the computer 1000, and the like.

  The HDD 1400 stores a program executed by the CPU 1100, data used by the program, and the like. The communication interface 1500 receives data from another device via the network N, sends the data to the CPU 1100, and transmits data generated by the CPU 1100 to the other device via the network N.

  The CPU 1100 controls output devices such as a display and a printer and input devices such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 acquires data from an input device via the input / output interface 1600. The CPU 1100 also outputs the generated data to the output device via the input / output interface 1600.

  The media interface 1700 reads a program or data stored in the recording medium 1800 and provides the CPU 1100 with the program via the RAM 1200. The CPU 1100 loads such a program from the recording medium 1800 onto the RAM 1200 via the media interface 1700 and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a digital versatile disc (DVD) or a phase change rewritable disc (PD), a magneto-optical recording medium such as a magneto-optical disk (MO), a tape medium, a magnetic recording medium, or a semiconductor memory. Etc.

  For example, when the computer 1000 functions as the information processing server 10 according to the embodiment, the CPU 1100 of the computer 1000 performs control by executing processing based on a program or data (for example, model M) loaded on the RAM 1200. Implement part 13. The CPU 1100 of the computer 1000 reads and executes these programs or data (for example, model M) from the recording medium 1800, but as another example, these programs or data (for example, via the network N) from other devices Model M) may be acquired.

  Although some of the embodiments of the present application have been described in detail based on the drawings, these are only examples, and various modifications can be made based on the knowledge of those skilled in the art, including the aspects described in the rows of the disclosure of the invention. It is possible to implement the invention in other improved forms.

[6. effect〕
According to the present embodiment, the information processing server 10 acquires a question sentence. Then, the information processing server 10 generates association degree information indicating the association between the question sentence and each of the plurality of information services, based on the attribute information on the attribute of the question sentence. As a result, since the information processing server 10 knows from which information service the information related to the question sentence should be obtained, it is possible to realize appropriate support for the user who answers the question.

  The information processing server 10 extracts a predetermined text from the question sentence. Then, the information processing server 10 selects a predetermined information service from among a plurality of information services based on the degree of association information. Then, the information processing server 10 acquires related information related to the predetermined text from the predetermined information service. As a result, the information processing server 10 can use the text extracted from the question sentence as a query instead of merely using the full text of the question sentence as a query, so that it is possible to obtain more appropriate information for supporting the user's answer. it can.

  The information processing server 10 extracts a predetermined word or a predetermined sentence included in the question sentence as a predetermined text. As a result, the information processing server 10 can obtain information appropriate for the user's answer support based on the word or sentence extracted from the question sentence.

  The information processing server 10 uses the model generated by learning based on the first text and the second text that is at least one of the key word and the key sentence included in the first text to generate a question sentence from the question sentence Extract a predetermined word or a predetermined sentence included in. As a result, the information processing server 10 can obtain information appropriate for the user's answer support based on the key word or key sentence extracted from the question sentence.

  The information processing server 10 selects one or more information services from among a plurality of information services in descending order of the degree of association with the question sentence based on the degree of association information. Then, the information processing server 10 acquires related information from the selected information service. As a result, the information processing server 10 can acquire the related information from the information service that conforms to the question sentence, so that appropriate support for the user who answers the question can be realized.

  The information processing server 10 transmits content that is content related to a question sentence and that includes information related to related information. Thus, the information processing server 10 can realize appropriate support for the user who answers the question.

  The information processing server 10 transmits content that is a content for answering a question sentence and includes related information or a link for displaying related information. Thus, the information processing server 10 can realize appropriate support for the user who answers the question.

  The information processing server 10 acquires, as attribute information, first strength information indicating the strength of the association between the question sentence and each of the plurality of attributes. Then, the information processing server 10 acquires second strength information indicating the strength of the association between each of the plurality of attributes and each of the plurality of information services. Then, the information processing server 10 generates association degree information based on the first strength information and the second strength information. As a result, the information processing server 10 can acquire the related information from the information service that conforms to the question sentence, so that appropriate support for the user who answers the question can be realized.

  Although some of the embodiments of the present application have been described above in detail based on the drawings, these are only examples, and various modifications can be made based on the knowledge of those skilled in the art, including the aspects described in the section of the description of the invention. It is possible to implement the invention in other improved forms.

  In addition, the "section (module, unit)" described above can be read as "means" or "circuit". For example, the generation unit can be read as a generation unit or a generation circuit.

DESCRIPTION OF SYMBOLS 1 ... Information processing system 10 ... Information processing server 11, 110 ... Communications part 12, 130 ... Storage part 121 ... Question information database 122 ... Model database 13, 150 ... Control part 131 ... Question sentence acquisition part 132 ... Learning part 133 ... Creation Unit 134 Extraction unit 135 Selection unit 136 Related information acquisition unit 137 Transmission unit 20 Service providing server 100 Terminal device 120 Input unit 140 Output unit 151 Display unit 152 Display control unit C1, C2 Content M1, M2 ... Model

Claims (10)

A first acquisition unit for acquiring a question sentence,
A generation unit that generates association degree information indicating the association between the question sentence and each of a plurality of information services based on attribute information on an attribute of the question sentence;
An information processing apparatus comprising: An extraction unit for extracting a predetermined text from the question sentence;
A selection unit for selecting a predetermined information service from the plurality of information services based on the degree of association information;
A second acquisition unit that acquires related information related to the predetermined text from the predetermined information service;
The information processing apparatus according to claim 1, further comprising: The extraction unit extracts a predetermined word or a predetermined sentence included in the question sentence as the predetermined text.
The information processing apparatus according to claim 2, characterized in that: The extraction unit uses the model generated by learning based on a first text and a second text that is at least one of an important word and an important sentence included in the first text to generate the question text from the question text. Extracting the predetermined word or the predetermined sentence included in the question sentence;
The information processing apparatus according to claim 3, characterized in that: The selection unit selects one or more information services from among the plurality of information services in the descending order of the degree of association with the question sentence based on the degree of association information,
The second acquisition unit acquires the related information from the information service selected by the selection unit.
The information processing apparatus according to any one of claims 2 to 4, characterized in that: The transmission unit further transmits a content related to the question text, the content including information related to the related information.
The information processing apparatus according to any one of claims 2 to 5, characterized in that. The transmission unit transmits content that is content for answering the question text and includes the related information or a link for displaying the related information.
7. An information processing apparatus according to claim 6, wherein: The generation unit is
Acquiring, as the attribute information, first strength information indicating strength of association between the question sentence and each of the plurality of attributes;
Acquiring second strength information indicating strength of association between each of the plurality of attributes and each of the plurality of information services;
Generating the degree of association information based on the first intensity information and the second intensity information;
The information processing apparatus according to any one of claims 1 to 7, characterized in that: An information processing method executed by the information processing apparatus;
A first acquisition process for acquiring a question sentence,
A generation step of generating association degree information indicating a relation between the question sentence and each of a plurality of information services based on attribute information on an attribute of the question sentence;
An information processing method comprising: A first acquisition procedure for acquiring a question sentence,
A generation procedure for generating association degree information indicating the association between the question sentence and each of a plurality of information services based on attribute information on an attribute of the question sentence;
An information processing program for making a computer execute.

Images