WO2022220119A1 - 情報処理システム、情報処理方法及びプログラム - Google Patents

情報処理システム、情報処理方法及びプログラム Download PDF

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WO2022220119A1
WO2022220119A1 PCT/JP2022/015868 JP2022015868W WO2022220119A1 WO 2022220119 A1 WO2022220119 A1 WO 2022220119A1 JP 2022015868 W JP2022015868 W JP 2022015868W WO 2022220119 A1 WO2022220119 A1 WO 2022220119A1
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information
causal
chain
information processing
processing system
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French (fr)
Japanese (ja)
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潔 和泉
泰紀 坂地
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University of Tokyo NUC
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University of Tokyo NUC
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

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  • the present invention relates to an information processing system, an information processing method, and a program.
  • Patent Document 1 discloses a causal relationship extraction system that extracts causal relationships between economic events and corporate performance.
  • Patent Literature 1 can extract a causal relationship, it cannot quantitatively predict an increase or decrease of a parameter.
  • the present invention provides an information processing system that allows users to more easily obtain quantitative prediction results regarding various economic events.
  • an information processing system includes a processor capable of executing a program to perform the following steps.
  • the obtaining step obtains the input information received from the user and preset reference information.
  • the reference information is information containing causes and effects that associate multiple causes and effects.
  • the extracting step extracts a related numerical index based on the acquired input information and reference information.
  • a related numerical index relates to at least one item appearing in a chain of causal relationships starting from a keyword included in the input information.
  • the user can more easily obtain quantitative prediction results regarding various economic events and the like.
  • FIG. 1 is a configuration diagram showing an information processing system 1 according to this embodiment
  • FIG. 2 is a block diagram showing the hardware configuration of the user terminal 2
  • FIG. 3 is a block diagram showing the hardware configuration of the server 3
  • FIG. 3 is a block diagram showing functions realized by a control unit 33 and the like in the server 3.
  • FIG. 10 is an activity diagram showing the flow of information processing regarding generation of reference information IF
  • FIG. 10 is an activity diagram showing the flow of information processing relating to the presentation of causal chain 4 and related numerical indices RI
  • 4 is a conceptual diagram for explaining causal chain 4
  • FIG. 2 is a diagram showing an example of a screen 5 of a display unit 24 visually recognized by a user
  • FIG. 2 is a diagram showing an example of a screen 5 of a display unit 24 visually recognized by a user;
  • FIG. 7A and 7B are diagrams showing another example of a screen 7 of the display unit 24 visually recognized by a user.
  • FIG. 7A and 7B are diagrams showing another example of a screen 7 of the display unit 24 visually recognized by a user.
  • FIG. 7A and 7B are diagrams showing another example of a screen 7 of the display unit 24 visually recognized by a user.
  • the program for realizing the software appearing in this embodiment may be provided as a non-transitory computer-readable medium (Non-Transitory Computer-Readable Medium), or may be downloaded from an external server. It may be provided as possible, or may be provided so that the program is activated on an external computer and the function is realized on the client terminal (so-called cloud computing).
  • the term “unit” may include, for example, a combination of hardware resources implemented by circuits in a broad sense and software information processing that can be specifically realized by these hardware resources.
  • various information is handled in the present embodiment, and these information are, for example, physical values of signal values representing voltage and current, and signal values as binary bit aggregates composed of 0 or 1. It is represented by high and low, or quantum superposition (so-called quantum bit), and communication and operation can be performed on a circuit in a broad sense.
  • a circuit in a broad sense is a circuit realized by at least appropriately combining circuits, circuits, processors, memories, and the like.
  • Application Specific Integrated Circuit ASIC
  • Programmable Logic Device for example, Simple Programmable Logic Device (SPLD), Complex Programmable Logic Device (CPLD), and field It includes a programmable gate array (Field Programmable Gate Array: FPGA)).
  • FIG. 1 is a configuration diagram showing an information processing system 1 according to this embodiment.
  • the information processing system 1 includes a user terminal 2 and a server 3, which are connected through a network 11. FIG. These components are further described.
  • the system exemplified by the information processing system 1 consists of one or more devices or components. Therefore, even the server 3 alone is an example of a system.
  • FIG. 2 is a block diagram showing the hardware configuration of the user terminal 2. As shown in FIG. The user terminal 2 has a communication section 21 , a storage section 22 , a control section 23 , a display section 24 and an input section 25 . electrically connected. Descriptions of the communication unit 21, the storage unit 22, and the control unit 23 are omitted because they are substantially the same as those of the communication unit 31, the storage unit 32, and the control unit 33 in the server 3, which will be described later.
  • the display unit 24 may be included in the housing of the user terminal 2 or may be externally attached.
  • the display unit 24 displays a screen of a graphical user interface (GUI) that can be operated by the user.
  • GUI graphical user interface
  • the display unit 24 will be described as being included in the housing of the user terminal 2 .
  • the input unit 25 may be included in the housing of the user terminal 2 or may be externally attached.
  • the input unit 25 may be integrated with the display unit 24 and implemented as a touch panel. With a touch panel, the user can input a tap operation, a swipe operation, or the like.
  • a switch button, a mouse, a QWERTY keyboard, or the like may be employed instead of the touch panel. That is, the input unit 25 receives an operation input made by the user. The input is transferred as a command signal to the control unit 23 via the communication bus 20, and the control unit 23 can execute predetermined control and calculation as necessary.
  • FIG. 3 is a block diagram showing the hardware configuration of the server 3. As shown in FIG. The server 3 has a communication section 31 , a storage section 32 and a control section 33 , and these constituent elements are electrically connected inside the server 3 via a communication bus 30 . Each component will be further described.
  • the communication unit 31 preferably uses wired communication means such as USB, IEEE1394, Thunderbolt (registered trademark), wired LAN network communication, etc., but wireless LAN network communication, mobile communication such as 3G/LTE/5G, BLUETOOTH (registered trademark), etc. Communication and the like may be included as desired. That is, it is more preferable to implement as a set of these communication means. That is, the server 3 communicates various information with the user terminal 2 via the network 11 via the communication unit 31 .
  • wired communication means such as USB, IEEE1394, Thunderbolt (registered trademark), wired LAN network communication, etc.
  • wireless LAN network communication mobile communication such as 3G/LTE/5G, BLUETOOTH (registered trademark), etc. Communication and the like may be included as desired. That is, it is more preferable to implement as a set of these communication means. That is, the server 3 communicates various information with the user terminal 2 via the network 11 via the communication unit 31 .
  • the storage unit 32 stores various information defined by the above description. For example, it can be used as a storage device such as a solid state drive (SSD) for storing various programs related to the server 3 executed by the control unit 33, or as a temporary storage device related to program calculation. It can be implemented as a memory such as a random access memory (RAM) that stores various information (arguments, arrays, etc.). A combination of these may also be used. In particular, the storage unit 32 stores various programs related to the server 3 that are executed by the control unit 33 .
  • SSD solid state drive
  • RAM random access memory
  • the control unit 33 processes and controls overall operations related to the server 3 .
  • the control unit 33 is an example of a processor, such as a central processing unit (CPU) (not shown).
  • the control unit 33 implements various functions related to the server 3 by reading a predetermined program stored in the storage unit 32 . That is, information processing by software stored in the storage unit 32 can be specifically realized by the control unit 33 which is an example of hardware, and can be executed as each function unit included in the control unit 33 . These are further detailed in the next section.
  • the control unit 33 is not limited to a single unit, and may be implemented to have a plurality of control units 33 for each function. A combination thereof may also be used.
  • FIG. 4 is a block diagram showing functions realized by the control unit 33 and the like in the server 3.
  • the server 3 as an example of the information processing system 1 includes an acquisition unit 331 , a reference information generation unit 332 , a chain processing unit 333 and a display control unit 334 .
  • the acquisition unit 331 is configured to acquire various information necessary for information processing.
  • the acquisition unit 331 may read various information stored in the storage unit 32 in advance, or acquire various information from an external device via the network 11 and the communication unit 31 .
  • the acquisition unit 331 acquires the input text KW (input information) received from the user and the preset causal information database IF1 (reference information IF). This will be explained in more detail later.
  • the reference information generation unit 332 is configured to generate the reference information IF. More specifically, the reference information generating unit 332 generates a causal information database IF1 (reference information IF) by extracting a plurality of causal factors based on the text data TX. This will be explained in more detail later.
  • a causal information database IF1 reference information IF1
  • the chain processing unit 333 is configured to generate a causal chain 4 that visually indicates a chained causal relationship. This will be explained in more detail later.
  • the display control unit 334 is configured to generate various types of display information and control display content that can be visually recognized by the user.
  • the display information may be information itself generated in a user-visible manner, such as screens, images, icons, texts, etc., or, for example, displays screens, images, icons, texts, etc. on the display unit 24 of the user terminal 2. It may be rendering information for rendering. More specifically, the display control unit 334 controls to display a screen including the related numerical index RI based on the acquired input text KW (input information) and causal information database IF1 (reference information IF). do. This will be explained in more detail later.
  • FIG. 5 is an activity diagram showing the flow of information processing regarding generation of reference information IF.
  • the reference information IF is information that the server 3 refers to when performing information processing related to generating a causal chain 4 (see FIG. 7) and presenting a related numerical index RI (see FIGS. 8 and 9), which will be described later.
  • the reference information IF may be a causal information database IF1 that includes causalities in which multiple causes and effects are associated.
  • the reference information IF may further include information that associates a plurality of keywords with related numerical indices RI.
  • the relevant numerical index RI associated with the extracted node 4N in the causal chain 4 can be easily presented.
  • the associated information is a learned model IF2 obtained by machine-learning in advance the correlation between a plurality of keywords and the related numerical index RI.
  • the generation of the reference information IF is illustrated as being performed by the server 3.
  • the user may operate the server 3 directly, or may remotely operate the server 3 using the user terminal 2 or the like by various methods represented by remote desktop or the like.
  • the user continuously generates the causal information database IF1, which is an example of the reference information IF, and the learned model IF2 for presenting the related numerical index RI.
  • a user who wants to construct a causal information database IF1 which is an example of reference information IF, selects arbitrary text data TX.
  • the acquisition unit 331 in the server 3 acquires at least one piece of text data TX.
  • the text data TX may be, for example, news, topic articles, academic papers, technical specifications, patent documents, research reports, and the like.
  • the text data TX is not limited to a text file, and may be a word processing software file, a PDF file, or an HTML file on the Internet (activity A001).
  • the control unit 33 in the server 3 reads the dedicated program stored in the storage unit 32 to analyze the text data TX selected in activity A001.
  • causal sets relating to various events are extracted from the text data TX (activity A002).
  • a method for extracting causal pairs is not particularly limited, and an existing natural language processing algorithm may be appropriately adopted.
  • the control unit 33 in the server 3 reads out the dedicated program stored in the storage unit 32 to add the causal information database IF1 to the causal information database IF1 by adding the causal group extracted in the activity A002. Generate (activity A003).
  • the reference information generating unit 332 generates the causal information database IF1 (reference information IF) by extracting a plurality of causal factors based on the text data TX. According to this aspect, it is possible to generate the causal information database IF1 according to the user's preference and the desired usage environment, so that it is possible to realize the prediction of an event with a more limited application.
  • the user can select multiple text data TX. For example, after the causal information database IF1 is generated using the text data TX selected for the first time, another text data TX can be selected for the second and subsequent processes. In other words, by appropriately repeating the activities A001 to A003, it is possible to generate the causal information database IF1 with a larger amount of information.
  • a user who wants to build a learned model IF2 which is an example of reference information IF, selects various keywords and numerical indices that serve as teacher data (activity A004). This may be the selection of the text data TX used to extract the causal pair, or the selection of different data.
  • the user inputs various selected keywords and numerical indices into an existing machine learning algorithm to cause the server 3 to perform machine learning (activity A005).
  • a learned model IF2 is generated (activity A006).
  • the user can update the learned model IF2 by repeating machine learning using additional teacher data. For example, after generating the trained model IF2 using the keywords and numerical indices selected the first time, as the second and subsequent processes, it is possible to perform further machine learning using different teacher data as input. In other words, by repeating activities A004 to A006 as appropriate, it is possible to generate a more accurate learned model IF2.
  • FIG. 6 is an activity diagram showing the flow of information processing regarding the presentation of the causal chain 4 and the associated numerical index RI.
  • the acquisition unit 331 acquires the input text KW (input information) received from the user and the (preset) reference information IF generated by the information processing shown in FIG. Activity A 102). That is, the control unit 33 matches the input text KW received through the communication unit 31 with the causal information database IF1 stored in the storage unit 32, thereby searching for a causal set suitable for the input text KW (activity A103). , determine this (activity A104). Examples of causal sets are described below.
  • control unit 33 inputs the keyword included in the causal pair to the learned model IF2 stored in the storage unit 32, so that the related numerical index RI highly related to the keyword is obtained. proposed. An example of the related numerical index RI will be described later.
  • a screen such as screen 5 is displayed in a manner that allows the user to grasp the set of determined causality and the proposed related numerical index RI (activity A105).
  • the control unit 33 reads out a predetermined program stored in the storage unit 32, and as an extraction step, based on the acquired input text KW (input information) and causal information database IF1 (reference information IF) to extract the related numerical index RI.
  • the display control unit 334 controls to display the screen 5 or the like including the extracted related numerical index RI.
  • the user grasps the related numerical index RI as well as the cause and effect of various events related thereto. can do. That is, the user can obtain a more quantitative prediction result than in the past.
  • the user may optionally enter a different input text KW of interest next.
  • the user can obtain prediction results for a plurality of events.
  • another similar causal set can be searched continuously. Specifically, first, the user selects one of the causal pairs (node 4N) already displayed on the display unit 24 using the input unit 25 (activity A106).
  • control unit 33 reads out the reference information IF (activity A107), and compares the selected causal pair with the causal information database IF1 stored in the storage unit 32 to determine the relationship between the causal pair and the relationship. search for and determine a causal set of (activity A108). Examples of relevant causal sets are described below.
  • control unit 33 inputs a keyword included in the causal pair into the learned model IF2 stored in the storage unit 32, thereby making the keyword highly related to the keyword.
  • a new relevant numerical index RI is proposed (activity A109).
  • screens such as screen 5 are updated in a manner that allows the user to grasp the set of determined causality and the proposed related numerical index RI (activity A110).
  • the user repeats the activities A101 to A105 or A106 to A110 as appropriate to generate the causal chain 4, which is visibly displayed to the user.
  • the chain processing unit 333 which is a functional unit of the control unit 33, can generate the causal chain 4 visually showing the chained causal relationship as a chain processing step. Then, along with the causal chain 4, a relevant numerical index RI associated with each causal set will be proposed.
  • this information processing method includes each step of the information processing system 1, and more specifically, includes the following steps.
  • the acquisition step the input text KW (input information) received from the user and the preset causal information database IF1 (reference information) are acquired.
  • the causal information database IF1 (reference information) is information containing causalities in which multiple causes and effects are associated.
  • the extraction step the related numerical index RI is extracted based on the acquired input text KW (input information) and causal information database IF1 (reference information).
  • the related numerical index RI is related to at least one item appearing in a chain of causal relationships starting from the keyword included in the input text KW (input information).
  • FIG. 7 is a conceptual diagram for explaining the causal chain 4.
  • FIG. 8 and 9 are diagrams showing an example of the screen 5 of the display unit 24 visually recognized by the user.
  • the causal chain 4 is a network structure that hierarchically has nodes 4N representing causal pairs.
  • a causal chain 4 indicates a chain of causal relationships starting from the input text KW entered by the user. More specifically, the causal chain 4 includes multiple nodes 4N that indicate causality in the causal information database IF1.
  • a node 41, a node 42, and a node 43 which are nodes 4N representing a set of three causal effects, are presented based on the causal information database IF1.
  • causal information database IF1 is an example of reference information IF preset based on text data TX.
  • nodes 411, 412 and 413 are presented as nodes 4N following node 41.
  • nodes 421, 422 and 423 are presented as nodes 4N following node 42, related to the causal set shown at node 42, and related to the causal set shown at node 43, node 431 , node 432 and node 433 are presented as node 4 N following node 43 .
  • Such a causal chain 4 makes it possible to form a causal stream of related events.
  • the causal effects shown in the nodes 4N connected to each other are causal effects similar to each other.
  • the plurality of nodes 4N are configured to be able to calculate similarities with each other.
  • the causal chain 4 has a given causal chain 4' containing a plurality of nodes 4N in series. For example, in FIG. 7, input text KW, node 41 and node 411 correspond to a given causal chain 4'.
  • the chain processing unit 333 adds a new node 4N succeeding the terminal node 4N of the predetermined causal chain 4', and the added new node 4N is similar to the terminal node 4N by a predetermined value or more. degree.
  • the concept of similarity is not particularly limited, and the similarity may be determined based on the distance defined in existing natural language processing, for example.
  • the causal chain 4 may be presented on the screen 5 as shown in FIG.
  • the user is inputting "infectious disease” into the input field 50 as the input text KW while referring to the screen 5 .
  • a node 4N representing a set of causes and effects consisting of a first cause 51 and a first result 52 is displayed in the region R1.
  • the first cause 51 includes the keywords "world situation”, “infectious disease” and “influence”, and the first results 52 resulting from these are "Japanese”, “Departure company”, “Year-on-year change” and the keyword “many people”.
  • the region R2 further displays a node 4N indicating a causal set consisting of the second cause 61 and the second effect 62.
  • the second causes 61 include the keywords “Japanese,” “guests,” and “decrease,” and the resulting second results 62 are "supply and demand,” “mitigation,” “direction,” and “guest rooms.” ” and “unit price” are included.
  • each time the node 4N is added a new area and the corresponding node 4N should be displayed one after another under the area R2.
  • the method of selecting the terminal node 4N when adding the succeeding node 4N is not particularly limited. , a new node 4N is displayed in the region R2, or the like.
  • a first cause 51 and its result, a first result 52 are presented.
  • a second result 62 which is a further result thereof, is presented. That is, the causal chain 4 is presented in the direction of pursuing the effect from the cause.
  • the associated numerical index RI associated with second result 62 is presented along with causal chain 4. That is, the related numerical index RI is related to at least one item that appears in a chain of causal relationships starting from the input text KW. At least one item corresponds to a "guest room" here. More specifically, the associated numerical index RI is associated with at least one node 4N appearing in the causal chain 4; Further, in generating such a screen 5, the display control unit 334 controls to display a screen including the causal chain 4 and the related numerical index RI. According to this aspect, since the related numerical index RI is displayed on the screen together with the causal chain 4 in a glanceable manner, the user can comprehend the flow of causality and the related numerical index RI from a bird's-eye view. can do.
  • the trained model IF2 can be referred to when presenting the related numerical index RI.
  • a method for determining the degree of association is not particularly limited, but more specifically, it may be determined based on a modified odds ratio as shown in Equation 1, for example.
  • the degree of unrelevance may be determined and the numerical relevance index RI may be presented.
  • the keywords included in the set of causality indicated by the node 4N include keywords indicating a large or small increase or decrease, such as "decrease”, “increase”, “significant”, or “minor”.
  • a node 4N of may have a polarity representing a numerical increase or decrease, and the associated numerical index RI may be determined based on the polarity. In predicting economic events, etc., increasing or decreasing changes are extremely important. Considering the directionality of the change in polarity along the causal chain 4, it is possible to present a preferable related numerical index RI desired by the user, which is a user-friendly specification. In addition to presenting the related numerical index RI, it may be implemented so as to obtain a predicted value of the numerical value.
  • the information processing system 1 searches for causal relationships such as economic events in a chained manner from the causal information database IF1 extracted from the text data TX, and presents the related numerical index RI related to the ripple effect. Proposed. According to such an information processing method by the information processing system 1, the user can more easily obtain quantitative prediction results regarding various economic phenomena and the like.
  • a program that causes a computer to execute each step of the information processing system 1 may be provided.
  • Such a program may be configured to be executable offline on a stand-alone computer separated from the network 11 .
  • the related numerical index RI When presenting the related numerical index RI, it may be implemented not only to present the index itself, but also to obtain a predicted value of time change, such as an upward change in the value after X days or within X months.
  • At least one text data TX selected for generating reference information such as causal information database IF1 may be a plurality of text data TX written in different languages.
  • the first time, text data TX written in Japanese is used to extract causal pairs
  • the second time, text data TX written in a foreign language such as English or Chinese is used to extract causal pairs.
  • a new node 4N related to the terminal node 4N is searched for and determined based on the causal information database IF1.
  • the causal information database IF1 may be implemented.
  • other nodes 4N located in parallel in the same hierarchy may be referred to.
  • not only the immediately preceding node 4N but also a new node 4N that follows the previous causal flow is proposed, so that the user can make more accurate predictions.
  • the causal chain 4 is presented in the direction of pursuing the effect from the cause, but the user is prompted to enter the input text KW regarding the result, and the causal chain is shown in the direction of pursuing the cause from the result. It may be implemented to present chain 4.
  • 10 and 11 are diagrams showing another example of the screen 7 of the display unit 24 visually recognized by the user.
  • FIG. 10 the user is inputting "infectious disease" into the input field 70 as the input text KW while referring to the screen 7.
  • nodes 81, 82, 83, and a plurality of nodes 4N are presented on the right side of the input field 70.
  • the screen 7 is provided with a selection button 76 and a selection button 77 below the input field 70 .
  • the select button 76 When the user selects the select button 76, the causal chain 4 can be displayed in the direction of pursuing the effect from the cause.
  • the selection button 77 when the user selects the selection button 77, the causal chain 4 can be displayed in the direction of pursuing the cause from the effect.
  • one item causes a plurality of results, it is considered to be a forward problem to present the direction to pursue the result from the cause.
  • constraint conditions are not particularly limited, and may be assumed as appropriate, such as changing the weighting value, limiting the subsequent node 4N, and the like.
  • the causal chain 4 can theoretically be added almost endlessly as long as the information in the causal information database IF1 is sufficient. It is assumed that the relationship with KW will become weaker. To prevent this, it may be implemented to limit the hierarchy of nodes 4N that can be included in one causal chain 4.
  • FIG. Although the number of layers to be restricted is not particularly limited, specifically, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 layers, and may be in the range between any two of the values exemplified herein. It is more preferably 1 to 5 layers, and particularly preferably 3 layers.
  • the causal chain 4 and the related numerical index RI are generated with the input text KW as the starting point, and an example in which these are presented has been described. It may be implemented such that a causal chain 4 containing 4N is presented.
  • the acquisition unit 331 acquires the numerical index (related numerical index RI) received from the user and preset reference information IF as an acquisition step.
  • the reference information IF is information that associates a plurality of keywords with numerical indices, and is, for example, a learned model IF2.
  • the control unit 33 extracts the causal chain 4, which is a chained causal relationship including the keyword, based on the acquired numerical index and reference information.
  • the information processing system is configured to further execute a display control step, wherein the display control step controls to display a screen including the extracted related numerical index.
  • the reference information further includes information that associates a plurality of keywords with the related numerical index.
  • the associated information is a learned model obtained by previously machine-learning the correlation between the plurality of keywords and the associated numerical index.
  • the information processing system further comprising a chain processing step, wherein the chain processing step generates a causal chain visually indicating a chained causal relationship, wherein the causal chain indicates causality in the reference information including a plurality of nodes, wherein the display control step controls display of a screen including the causal chain and the related numerical index, the related numerical index being related to at least one node appearing in the causal chain ,thing.
  • the plurality of nodes are configured to be able to calculate similarity with each other
  • the causal chain includes a predetermined causal chain including a plurality of serially connected nodes
  • the chain processing step includes the predetermined adding a new node succeeding the terminal node of the causal chain of , wherein the added new node has similarity with the terminal node equal to or greater than a predetermined value.
  • a plurality of nodes in the predetermined causal chain are weighted, and new subsequent nodes are determined based on the weighted evaluation values.
  • the plurality of nodes have polarities representing numerical increases or decreases, and the associated numerical indices are determined based on the polarities.
  • the information processing system further includes a reference information generating step, wherein the obtaining step obtains at least one piece of text data, and the reference information generating step extracts a plurality of causal factors based on the text data, Generating said reference information.
  • the information processing system further comprises a chain processing step, wherein the at least one text data is a plurality of text data written in different languages, and the chain processing step visually indicates chain causal relationships. generating a causal chain, wherein said causal chain includes a plurality of nodes each representing causality described in said different languages.
  • An information processing system comprising a processor capable of executing a program so as to perform the following steps, in an obtaining step obtaining a numerical index received from a user and preset reference information, wherein The reference information is information that associates a plurality of keywords with the numerical index, and in the extracting step, a chain causal relationship including the keyword is extracted based on the obtained numerical index and the reference information. ,thing.
  • a method of information processing comprising steps of the information processing system.
  • a program that causes a computer to perform each step of the information processing system. Of course, this is not the only case.

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