Presentation device, presentation method, and presentation program

Abstract

Provided is a presentation device (10) wherein a storage unit (14) stores a risky expression dictionary (14a) that is information pertaining to the risk of language trouble for each word constituting an interaction text. A calculation unit (15b) refers to the risky expression dictionary (14a) and calculates a risk value representing the degree of risk for each word constituting an interaction text that was input.

Description

[Technical Field]

[0001] The present invention relates to a presentation device, a presentation method, and a presentation program. [Background technology]

[0002] In communication via language, expressions that may hinder mutual respect and understanding may be used, such as ambiguous expressions, overbearing expressions, expressions based on stereotypes, etc. Conventionally, a system has been proposed that instantly transcribes the content of utterances so that the expressions used can be easily reviewed after communication (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2020-140629 Summary of the Invention [Problem to be solved by the invention]

[0004] However, with conventional techniques, it is difficult to quantitatively analyze the risks inherent in expressions used in language-mediated communication.

[0005] The present invention has been made in view of the above, and aims to quantitatively analyze the risks inherent in expressions used in language-mediated communication. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the objectives, the presentation device of the present invention is characterized by having a memory unit that stores information regarding the risk of language trouble for each word that makes up a dialogue, and a calculation unit that refers to the memory unit and calculates a risk value that represents the magnitude of the risk for each word that makes up the input dialogue. [Effects of the Invention]

[0007] According to the present invention, it is possible to quantitatively analyze the risks inherent in expressions used in language-mediated communication. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a schematic diagram illustrating the general configuration of the presentation device of this embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of the data structure of the risk expression dictionary. [Figure 3] FIG. 3 is a diagram for explaining the risk expression dictionary. [Figure 4] FIG. 4 is a diagram illustrating an example of the data structure of the relationship DB. [Figure 5] FIG. 5 is a diagram illustrating an example of the data structure of the social attribute DB. [Figure 6] FIG. 6 is a diagram illustrating an example of the data structure of the individual characteristic DB. [Figure 7] FIG. 7 is a diagram illustrating an example of the data structure of the communication history DB. [Figure 8] FIG. 8 is a diagram for explaining the processing of the calculation unit. [Figure 9] FIG. 9 is a diagram showing an example of a screen display of the presentation process result. [Figure 10] FIG. 10 is a flowchart showing the procedure of the presentation process. [Figure 11] FIG. 11 is a diagram illustrating an example of a computer that executes a presentation program. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited to this embodiment. In addition, in the description of the drawings, the same parts are designated by the same reference numerals.

[0010] [Presentation device configuration] 1 is a schematic diagram illustrating the overall configuration of a presentation device according to the present embodiment. As illustrated in FIG. 1, a presentation device 10 according to the present embodiment is implemented by a general-purpose computer such as a personal computer, and includes an input unit 11, an output unit 12, a communication control unit 13, a storage unit 14, and a control unit 15.

[0011] The input unit 11 is realized using input devices such as a keyboard, mouse, camera, and microphone, and inputs various instruction information such as a command to start processing to the control unit 15 in response to an input operation by an operator. The output unit 12 is realized by a display device such as a liquid crystal display, a printing device such as a printer, etc. For example, the output unit 12 displays the results of the presentation processing described below.

[0012] The communication control unit 13 is realized by a NIC (Network Interface Card) or the like, and controls communication between the control unit 15 and an external device via a telecommunication line such as a LAN (Local Area Network) or the Internet. For example, the communication control unit 13 controls communication between the control unit 15 and an external management device or the like that manages various types of information.

[0013] The storage unit 14 is realized by a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 14 stores in advance the processing program that operates the presentation device 10, data used during execution of the processing program, and the like, or temporarily stores the data each time processing is performed. The storage unit 14 may be configured to communicate with the control unit 15 via the communication control unit 13.

[0014] In this embodiment, the storage unit 14 stores a risk expression dictionary 14a. The risk expression dictionary 14a includes information about the risk of language trouble for each word constituting the dialogue. Specifically, the risk expression dictionary 14a includes information about one or more of the risks of the part of speech of each word, the number of meanings indicated by each word, and the ambiguity of the meaning indicated by each word. This information is collected via the input unit 11 or from a management device that manages various information, etc., prior to or during the presentation process described below, and is stored in the storage unit 14.

[0015] Here, Fig. 2 is a diagram illustrating an example of the data structure of the risk expression dictionary. Also, Fig. 3 is a diagram for explaining the risk expression dictionary. First, as illustrated in Fig. 2, the risk expression dictionary 14a includes information items such as part of speech, meaning, number of meanings, degree of danger, risk type, etc. for each word.

[0016] The number of meanings is the number of different meanings for the same word. In addition, the number of homonyms may be added. The risk level is a value set by the user for each word, taking into account, for example, the part of speech, risk type, relevance to business, etc. Alternatively, the risk level may be a value based on a predefined classification table of ambiguous words, etc.

[0017] The risk type is, for example, a value ranging from 1 to 7 indicating each class of ambiguity classification. Here, Fig. 3 shows an example of seven ambiguity classes. For example, a risk type of 1 is set for a word with multiple meanings.

[0018] The risk expression dictionary 14a may include information items that change dynamically during use, such as the importance of each word in a sentence. For example, it may be possible to individually set a low risk value for a word that has a high risk value calculated in the presentation process described below, if the word does not have a high importance.

[0019] Furthermore, the risk expression dictionary 14a is not limited to information items for each word, but may include information items for each group of expressions made up of multiple words. In this case, expressions may include verbal expressions of facial expressions, etc.

[0020] The memory unit 14 also stores a relationship DB (Data Base) 14b, a social attribute DB 14c, an individual characteristic DB 14d, a communication history DB 14e, etc. The relationship DB (Data Base) 14b includes information about the relationships between speakers. The social attribute DB 14c includes information representing the social attributes of the speaker and the interlocutor. The individual characteristic DB 14d includes information representing the speaker's characteristics related to utterances. The communication history DB 14e also includes a dialogue history. This information is collected via the input unit 11 or from a management device that manages various information, prior to or during the presentation process described below, and stored in the memory unit 14.

[0021] Here, Fig. 4 is a diagram illustrating an example of the data configuration of the relationship DB. As illustrated in Fig. 4, the relationship DB 14b includes, for each user, information items representing the relationship with a specific other user, such as a relationship score, relationship information, and non-work interactions. Fig. 4 illustrates the relationship DB 14b of a user with a user ID of 002.

[0022] The relationship score ranges from 0.0 to 1.0, with values ​​closer to 1 indicating a closer relationship and values ​​closer to 0 indicating a more distant relationship. For example, since a partner user who frequently interacts with another user has a closer relationship, the relationship score may be calculated using the interaction frequency in the communication history DB 14e (described later). Alternatively, the relationship score may be set with reference to job titles in the social attribute DB 14c (described later), such that 0.5 is used for the same job title and 0.1 is used for jobs with a greater distance between them, such as between the president and an ordinary employee. Alternatively, the relationship score may be calculated comprehensively with reference to information items in the communication history DB 14e, social attribute DB 14c, and personal characteristic DB 14d (described later), or may be calculated from compatibility using evaluation values ​​of the company's corporate culture, the individual's personality, such as the Big 5, etc.

[0023] The relationship information indicates a business relationship between a user and a specific other user, such as a relationship with a superior in the same department, etc. The non-work-related interaction indicates whether or not there is interaction outside of work.

[0024] Fig. 5 is a diagram illustrating an example of the data configuration of the social attribute DB. As illustrated in Fig. 5, the social attribute DB 14c includes information items representing the social attributes, i.e., social positions, of the speaker and interlocutor, such as affiliation, position, and years of employment. The social attribute DB 14c is set for each topic or situation of the conversation. Fig. 5 illustrates the social attribute DB 14c for business topics.

[0025] Fig. 6 is a diagram illustrating an example of the data configuration of the individual characteristic DB. As illustrated in Fig. 6, the individual characteristic DB 14d includes, for each user, information items representing speaker characteristics related to each utterance, such as frequency, degree of agreement with usage status, frequently occurring topics, and risk value evaluation.

[0026] The frequency is a value ranging from 0.0 to 1.0 that indicates how often the statement occurs. The consistency of usage is a value ranging from 0.0 to 1.0 that indicates the consistency of the topics in which the statement occurs. The risk value evaluation is a value ranging from 0.0 to 1.0 that indicates the user's self-evaluation of the history of risk values ​​calculated for the word, with a higher value indicating a higher evaluation that the risk value is appropriate.

[0027] Fig. 7 is a diagram illustrating an example of the data configuration of the communication history DB. As illustrated in Fig. 7, the communication history DB 14e represents a dialogue history, and for each utterance, includes information items such as a utterance ID, a user ID, a time, the content of the utterance, a risk value, a topic, an interlocutor ID, and a record of miscommunication.

[0028] The risk value is a risk value previously calculated by the presentation process described below. The topic is a label that indicates the outline of the conversation in which the statement was made, and an appropriate social attribute DB 14c is selected based on this. The miscommunication record indicates whether or not there is a record of any trouble caused by the statement.

[0029] The communication history DB 14e may also include non-verbal communication expressions such as facial expressions based on video data or the like.

[0030] Returning to the explanation of FIG. 1, the control unit 15 is realized using a CPU (Central Processing Unit) or the like, and executes a processing program stored in memory. As a result, the control unit 15 functions as an acquisition unit 15a, a calculation unit 15b, and a presentation unit 15c, as exemplified in FIG. 1, and executes the presentation process. Note that these functional units may each be implemented in different hardware, or some of them may be implemented in different hardware. Furthermore, the control unit 15 may also include other functional units.

[0031] The acquisition unit 15a acquires words that constitute the dialogue to be processed. Specifically, the acquisition unit 15a accepts input of the dialogue to be processed via the input unit 11 or the communication control unit 13, and performs morphological analysis to extract words that constitute the dialogue.

[0032] The acquisition unit 15a may store the acquired words constituting the dialogue prior to the presentation process described below in the storage unit 14. Alternatively, the acquisition unit 15a may immediately transfer this information to the calculation unit 15b.

[0033] The calculation unit 15b refers to the risk expression dictionary 14a in the storage unit 14 and calculates a risk value that indicates the magnitude of risk for each word that makes up the input dialogue. Here, the risk value indicates an estimated value of the magnitude of the risk of language trouble, i.e., miscommunication, occurring for each word that appears in the sentence or for the entire sentence. The calculation unit 15b calculates the risk value using risk variables derived from information related to risk included in the risk expression dictionary 14a.

[0034] Here, the risk variable is derived based on, for example, the part of speech or the risk level of the word. Alternatively, the risk variable is derived based on the number of meanings of the word. Alternatively, the risk variable is derived based on the risk type. Note that risk variables related to context are not used.

[0035] The calculation unit 15b may also calculate a risk value for each group of expressions made up of multiple words, which is a unit of information in the risk expression dictionary 14a. In this case, the expressions may include verbal expressions of facial expressions.

[0036] The calculation unit 15b calculates a risk value using the product of multiple pieces of information about risks that have been weighted in a predetermined manner. Specifically, the calculation unit 15b calculates the risk value r of the i-th word in the sentence using the following formula (1): i Calculate.

[0037]

number

[0038] Here, the risk variable p i1 takes on three levels of value, from 1 to 3, with the higher the value, the higher the risk. For example, if the part of speech is a modifier such as an adjective or adverb, it is ambiguous and high risk, so it is set to 3, if it is a demonstrative it is set to 2, and otherwise it is set to 1. Alternatively, the risk level may be applied as is, or a value based on the part of speech may be used in combination with the risk level value.

[0039] Also, the risk variable p based on the number of meanings of a word i2 For example, the value registered in the risk expression dictionary 14a may be used as is, or the risk variable p i1 Similarly, the values ​​may be classified into three levels.

[0040] In addition, the risk variable p i3 For example, a value may be set by a user who is familiar with the business situation.

[0041] Also, the risk value r i The weight variables w1 to w3 and the risk variable p i1 ~p i3 is adjusted.

[0042] Here, Fig. 8 is a diagram for explaining the processing of the calculation unit. Fig. 8(1) shows a risk variable p i3 The risk level is defined in three levels, from level 1 to 3, as shown in Figure 8(2). Also, the risk variable p i2 is exemplified.

[0043] And, in Figure 8(3), the part of speech of the i-th word p i1 , meaning number p i2 , risk level p i3 Risk value r using i Here, in the example shown in FIG. 8(3), the part of speech p i1 Set the weight variable 1 (w1) to 0, and then i1 By suppressing the influence of the semantic number p i2 and risk level p i3 The risk value r i has been calculated.

[0044] In the example shown in FIG. 8(3), the risk value r i To prevent this from becoming 0, the part of speech p i1 × weight variable 1 (w1), number of meanings p i2 × Weight variable 2 (w2), risk level p i3 × 1 is added to each of the weight variables 3 (w3) and then multiplied.

[0045] Returning to the explanation of FIG. 1, the calculation unit 15b may calculate the risk value using the sum of multiple pieces of information related to risks that have been weighted in a predetermined manner. For example, the calculation unit 15b calculates the risk value r of the i-th word in the sentence using the following formula (2): i Calculate.

[0046]

number

[0047] In this case, each risk variable p i1 ~p i3 is a normalized value between 0.0 and 1.0, and the weight variables w1 to w3 are values ​​in the range of 0.0 to 1.0. As a result, the calculated risk value r i The values ​​are in the range of 0.0 to 1.0, making it easy to compare risk values.

[0048] The calculation unit 15b may further calculate the risk value using one or more of information about the relationship between speakers, information representing the social attributes of the speaker and the interlocutor, information representing the characteristics of the speaker, and the dialogue history. For example, the calculation unit 15b calculates the risk value by referring to the relationship DB 14b, the social attribute DB 14c, the personal characteristic DB 14d, or the communication history DB 14e. This makes it possible to calculate the risk value taking the context into consideration.

[0049] In this case, the calculation unit 15b calculates the risk value r of the i-th word in the sentence using, for example, the following formula (3): i Calculate.

[0050]

number

[0051] Here, the risk variable p i1 ~p i3 is the same as the above formula (1). The weight variables w1 to w7 are values ​​in the range of 0.0 to 1.0. The calculated risk value r i The weight variables w1 to w7 and the risk variable p i1 ~p i7 is adjusted.

[0052] Risk variable p based on the relationship between users i4For example, the risk variable p may be calculated by subtracting the relationship score of the relationship DB 14b from 1, or may be calculated by multiplying the relationship score by 1, where 2 is used if there is non-work-related interaction and 1 is used if there is no non-work-related interaction. i4 The weaker the relationship, the larger the value becomes, and the greater the risk value becomes.

[0053] Risk variable p based on social attributes i5 For example, the risk variable p may be the difference in the number of years of employment between users in the social attribute DB 14c, or may be the difference in the quantified job titles between users who are interacting, with the higher the job title, the larger the value. i5 The greater the difference in position within the organization, such as years of employment or position, the larger the value becomes, and the greater the risk value becomes.

[0054] Alternatively, the risk variable p calculated as above i4 and the risk variable p i5 The product or sum of the risk variable p i4 and the risk variable p i5 It may be substituted as:

[0055] Risk variable p based on individual characteristics i6 may be set to, for example, a value obtained by subtracting the degree of agreement of the usage situation of the individual characteristic DB 14d from 1, or may be set to the reciprocal of the degree of agreement of the usage situation. i6 may be the frequency in the individual characteristic DB 14d, or may be the product of the frequency and a value obtained by subtracting the degree of agreement of the usage situation from 1.

[0056] Risk variable p based on dialogue history i7 For example, the risk variable p may be a risk value in the communication history DB 14e, or may be a product of the risk value and a numerical value of the miscommunication record (2 if there is one, 1 if there is none). Alternatively, assuming that the closer the relationship to the business, the greater the risk of miscommunication, the risk variable p i7is a numerical value that quantifies the relevance of the topic to work, and may be set to a larger value as the relevance to work increases. For example, confidential information related to corporate strategy is set to 1, general internal information is set to 0.5, and public information related to after-work recreational events is set to 0.1.

[0057] Alternatively, the calculation unit 15b calculates the risk value r of the i-th word in the sentence using the following formula (4): i may be calculated.

[0058]

number

[0059] Here, the risk variable p i1 ~p i7 is a value obtained by normalizing the value to 0.0 to 1.0, similar to the above formula (3), and the weight variables w1 to w7 are values ​​in the range of 0.0 to 1.0.

[0060] In addition, the risk variable p i1 ~p i7 The values ​​are not limited to those described above. For example, by additionally registering a predetermined information item in each piece of information (14a to 14e) in the storage unit 14, values ​​such as those exemplified below may be applied.

[0061] For example, the risk variable p i1 ~p i3 The degree of occurrence of miscommunication due to the word, the degree of danger when miscommunication occurs due to the word, the frequency of occurrence of the word in general conversation, etc. may be applied as the criterion.

[0062] In addition, the risk variable p using the relationship DB14b i4 or risk variable p using social attribute DB14c i5 As examples, the common background of the speakers, the degree of strength or overlap of attributes, the proportion of common understanding between the speakers, the personal relationships between the speakers, etc. may be applied.

[0063] In addition, the risk variable p i6 As the risk assessment, the speaker's background, attributes, and an evaluation of the risk value calculated for past statements may be applied.

[0064] In addition, the risk variable p i7 As the target, words that appear frequently in the current dialogue, words that appear frequently in past dialogues, words that have caused miscommunication in past dialogues, words that have been commonly understood, etc. may be applied.

[0065] Furthermore, the method for calculating the risk value is not limited to using the information (14a to 14e) from the storage unit 14. For example, the risk value may be calculated by using the following information that can be collected during a conversation:

[0066] For example, the risk value may be calculated by combining the analysis results of biological signals such as facial expressions, electroencephalograms, and gaze, with the content of statements stored in the communication history DB 14e. Furthermore, an evaluation value of medical or objective changes in the physical condition of the person may be additionally applied as a risk variable.

[0067] Alternatively, the seriousness of a problem that occurs after a dialogue may be fed back and reflected in the risk or importance in the risk expression dictionary 14a or the risk value in the communication history DB 14e. The seriousness of this problem may be subjectively evaluated by the user and reflected in the risk or importance in the risk expression dictionary 14a. These pieces of information may also be used in combination.

[0068] The weight variables w1 to w7 are variables that adjust the influence of each risk variable, and are set according to the topic and participants of the conversation that is the subject of risk value calculation. For example, if there is a participant who is not good at guessing the meaning of ambiguous statements, by increasing the values ​​of the weight variables w1 to w3, the values ​​of the risk expression dictionary 14a will be more strongly reflected in the risk value.

[0069] In addition, in cases where participants in frequently held meetings have a deep understanding of the background of each other's comments, the weight variable w7 for the value in the communication history DB 14e is increased, so that the value of the risk variable based on expressions that appear frequently or expressions that are closely related to business operations is more strongly reflected in the risk value.

[0070] In addition, when there is a specific relationship such as one-on-one dialogue between a superior and a subordinate, the values ​​of the weight variables w4 and w5 are increased, so that the risk variable p i4 , risk variable p based on social attribute DB14c i5 The value of will be strongly reflected in the risk value.

[0071] In addition, when a conversation between speakers with specific excellent skills contains statements or expressions that are unique to that individual, the values ​​of the weight variables w4 and w6 are increased, so that the risk variable p i4 , risk variable p based on personal characteristics DB14d i5 The value of will be strongly reflected in the risk value.

[0072] In addition, the weight variables w1 to w7 may be dynamically adjusted based on a judgment of the quality of consensus reached in the most recent dialogue or discussion, measurements of satisfaction with past dialogues, measurements of proficiency with the topic that is the subject of the conversation, etc.

[0073] The risk value of each word calculated by the above formulas (1) to (4) can also be used to calculate the risk of an entire sentence or a group of expressions (for example, a phrase or clause) made up of multiple words. In this case, the risk value r calculated for the words contained in each sentence is i The average value of the risk values ​​r1 to r N The risk value of the sentence may be calculated by weighting each value of r based on the risk level or importance in the risk expression dictionary 14a or a preset value. i The sum, product, etc. can also be used.

[0074] The presentation unit 15c presents to the user presentation information generated based on the calculated risk value. For example, the presentation unit 15c presents to the user via the output unit 12 the risk value calculated by the calculation unit 15b or presentation information such as a message, a notification sound, or a vibration generated based on the risk value.

[0075] 9 is a diagram showing an example of a screen display of the results of the presentation process. In the example shown in FIG. 9, information on the calculated risk values ​​is presented on an online conference tool. For example, the presentation unit 15c displays text of words with high risk values ​​by highlighting them. For example, the presentation unit 15c changes the color of the text of words with high risk values, underlines them, makes the font larger, makes them bold, or highlights them.

[0076] Alternatively, the presentation unit 15c displays text with a high risk value in a format that attracts the user's attention, such as by displaying the text as a pop-up. Furthermore, the presentation unit 15c may display words with a high risk value in a position that is easily noticeable to the user.

[0077] Furthermore, instead of or in addition to displaying text, the presentation unit 15c may present words with high risk values ​​using audio information or tactile information. For example, the presentation unit 15c may increase the volume of the notification sound for words with high risk values, use a sound that is likely to make the user feel the need to take action, such as an incorrect answer sound, increase the vibration, or use a vibration frequency or pattern that is likely to attract attention.

[0078] Furthermore, the presenting unit 15c may display a CG or image agent on the screen and present words with high risk values ​​in the form of advice from the agent.

[0079] 9, the presentation unit 15c may present the same screen to user 1 and user 2, or may present different screens. For example, when the presentation unit 15c presents the same screen, both users are more likely to feel equal, and mutual understanding is promoted. On the other hand, when the presentation unit 15c presents different screens, although the promotion of mutual understanding is reduced, users with better communication skills can more flexibly engage in dialogue, thereby improving the efficiency of communication.

[0080] Furthermore, the presentation unit 15c may adjust the presentation method, cancel the presentation itself, etc. For example, the presentation unit 15c may present the information in a different format, such as by reducing the degree of highlighting or by stopping the highlighting for a user who is bothered by the highlighting, or by changing visual information to sound information, in response to a user operation or user characteristics. Furthermore, the user characteristics may be reflected differently depending on the composition of the conference participants.

[0081] [Proposal Processing] Next, a presentation process by the presentation device 10 according to this embodiment will be described with reference to Fig. 10. Fig. 10 is a flowchart showing the procedure of the presentation process. The flowchart in Fig. 10 starts, for example, when a user performs an operation input to instruct the start of the process.

[0082] First, the acquisition unit 15a acquires words that constitute a dialogue to be processed (step S1). For example, the acquisition unit 15a accepts input of the dialogue to be processed via the input unit 11 or the communication control unit 13, and performs morphological analysis to extract words that constitute the dialogue.

[0083] Next, the calculation unit 15b refers to the risk expression dictionary 14a and calculates a risk value that indicates the magnitude of the risk of language trouble for the words that make up the input dialogue (step S2). For example, the calculation unit 15b calculates the risk value using the product of multiple pieces of information in the risk expression dictionary 14a that have been weighted in a predetermined manner. Alternatively, the calculation unit 15b calculates the risk value using the sum of multiple pieces of information in the risk expression dictionary 14a that have been weighted in a predetermined manner.

[0084] Furthermore, the calculation unit 15b calculates the risk value using one or more pieces of information from the relationship DB 14b, the social attribute DB 14c, the personal characteristic DB 14d, and the communication history DB 14e.

[0085] Furthermore, the presentation unit 15c presents the presentation information generated based on the calculated risk value to the user (step S3). For example, the presentation unit 15c presents the risk value calculated by the calculation unit 15b to the user via the output unit 12. This completes a series of presentation processes.

[0086] [Other embodiments] In the presentation device 10 of the above embodiment, the risk value is calculated based on an ambiguous expression, but this is not limiting. For example, the presentation device 10 can calculate a risk value for a stereotyped expression instead of or in addition to the risk value based on an ambiguous expression. In this case, the calculation unit 15b calculates the risk value using information on the risk based on the stereotype.

[0087] For example, the risk variable p based on the risk expression dictionary 14a i1 ~p i3 As the risk level, a risk type set from the perspective of stereotypes, ambiguous expressions, or the like may be applied.

[0088] In addition, the risk variable p using the relationship DB14b i4 or risk variable p using social attribute DB14c i5 As the criteria, the background common to the speakers, the degree of strength of attributes, the degree of overlap, etc. may be applied.

[0089] In addition, the risk variable p i6 The speaker's background, attributes, etc. may be applied as the context.

[0090] Specifically, for example, when there is little overlap between the speakers' common backgrounds, there is a high possibility that the speakers will misinterpret the language, and the risk variable p i4 , risk variable p i6 The value of the risk variable p is set to a large value. In addition, the background of the speaker, such as gender and age, is compared with the background of the parties in past trouble cases, and the risk value in past trouble cases is used to set the risk variable p i6 This enables the calculation unit 15b to calculate a risk value based on a stereotype.

[0091] [effect] As described above, in the presentation device 10 of this embodiment, the storage unit 14 stores the risk expression dictionary 14a, which is information about the risk of language trouble for each word that makes up the dialogue. The calculation unit 15b refers to the risk expression dictionary 14a and calculates a risk value that indicates the magnitude of risk for each word that makes up the input dialogue.

[0092] Specifically, the risk expression dictionary 14a stores information relating to one or more risks, such as the part of speech of each word, the number of meanings indicated by each word, or the ambiguity of the meaning indicated by each word.

[0093] Furthermore, the calculation unit 15b calculates a risk value using the product of information about multiple risks that have been weighted in a predetermined manner, or the calculation unit 15b calculates a risk value using the sum of information about multiple risks that have been weighted in a predetermined manner.

[0094] This allows the presentation device 10 to quantitatively analyze the risks inherent in expressions used in language-mediated communication.

[0095] Furthermore, the calculation unit 15b calculates the risk value using one or more of the relationship DB 14b containing information about the relationship between speakers, the social attribute DB 14c containing information representing the social attributes of the speaker and interlocutor, the personal characteristic DB 14d containing information representing the speaker's characteristics related to utterances, and the communication history DB 14e containing the dialogue history, thereby enabling the presentation device 10 to calculate the risk value with higher accuracy.

[0096] Furthermore, the calculation unit 15b calculates the risk value using information about risks based on stereotypes, which also makes it possible to calculate a risk value with higher accuracy.

[0097] [program] A program written in a computer-executable language may be created to execute the processes executed by the presentation device 10 according to the above embodiment. In one embodiment, the presentation device 10 can be implemented by installing a presentation program that executes the above presentation process as package software or online software on a desired computer. For example, the information processing device can function as the presentation device 10 by executing the presentation program on the information processing device. The information processing device referred to here includes desktop and notebook personal computers. Other examples of information processing devices include mobile communication terminals such as smartphones, mobile phones, and PHS (Personal Handyphone Systems), as well as slate terminals such as PDAs (Personal Digital Assistants). The functions of the presentation device 10 may also be implemented on a cloud server.

[0098] 11 is a diagram showing an example of a computer that executes a presentation program. The computer 1000 includes, for example, a memory 1010, a CPU 1020, a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070. These components are connected by a bus 1080.

[0099] The memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM 1012. The ROM 1011 stores, for example, a boot program such as a BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to a hard disk drive 1031. The disk drive interface 1040 is connected to a disk drive 1041. A removable storage medium such as a magnetic disk or optical disk is inserted into the disk drive 1041. The serial port interface 1050 is connected to, for example, a mouse 1051 and a keyboard 1052. The video adapter 1060 is connected to, for example, a display 1061.

[0100] Here, the hard disk drive 1031 stores, for example, an OS 1091, an application program 1092, a program module 1093, and program data 1094. Each piece of information described in the above embodiment is stored in the hard disk drive 1031 or memory 1010, for example.

[0101] The presentation program is stored in the hard disk drive 1031 as a program module 1093 in which instructions to be executed by the computer 1000 are written. Specifically, the program module 1093 in which each process executed by the presentation device 10 described in the above embodiment is written is stored in the hard disk drive 1031.

[0102] Furthermore, data used for information processing by the presentation program is stored as program data 1094, for example, in the hard disk drive 1031. Then, the CPU 1020 reads the program module 1093 and the program data 1094 stored in the hard disk drive 1031 into the RAM 1012 as necessary, and executes each of the above-described procedures.

[0103] The program module 1093 and program data 1094 related to the presentation program are not limited to being stored in the hard disk drive 1031, but may be stored in a removable storage medium and read by the CPU 1020 via the disk drive 1041, etc. Alternatively, the program module 1093 and program data 1094 related to the presentation program may be stored in another computer connected via a network such as a LAN or a WAN (Wide Area Network), and read by the CPU 1020 via the network interface 1070.

[0104] Although the present invention has been described above as an embodiment, the present invention is not limited to the description and drawings that form part of the disclosure of the present invention. In other words, other embodiments, examples, and operational techniques that can be made by those skilled in the art based on the present invention are all included in the scope of the present invention. [Explanation of symbols]

[0105] 10 Presentation device 11 Input section 12 Output section 13 Communication control section 14 Storage section 14a Risk Expression Dictionary 14b Relationship DB 14c Social attribute DB 14d Personal characteristics DB 14e Communication History DB 15 Control Unit 15a Acquisition part 15b Calculation part 15c Tip Section

Claims

1. a storage unit that stores, for each word constituting the dialogue, information relating to the risk of language trouble, including at least two of the part of speech of each word, the number of meanings indicated by each word, and the ambiguity of the meaning indicated by each word; a calculation unit that refers to the storage unit and calculates a risk value that indicates the magnitude of the risk of each word that constitutes the input dialogue; A presentation device comprising:

2. The presentation device according to claim 1 , wherein the calculation unit calculates the risk value using a product of a plurality of pieces of information about the risk that have been weighted in a predetermined manner.

3. The presentation device according to claim 1 , wherein the calculation unit calculates the risk value using a sum of a plurality of pieces of information about the risk that have been weighted in a predetermined manner.

4. The presentation device described in claim 1, characterized in that the calculation unit further calculates the risk value using one or more of information regarding the relationship between speakers, information representing the social attributes of the speaker and interlocutor, information representing the speaker's characteristics regarding the utterance, or dialogue history.

5. The presentation device according to claim 1 , wherein the calculation unit calculates the risk value using information about the risk based on stereotypes.

6. A presentation method executed by a presentation device, the presentation device has a storage unit that stores, for each word constituting a dialogue, information relating to the risk of language trouble, including at least two of the part of speech of each word, the number of meanings indicated by each word, and the ambiguity of the meaning indicated by each word; A presentation method characterized by comprising a calculation step of referring to the storage unit and calculating a risk value representing the magnitude of the risk of each word constituting the input dialogue.

7. A presentation program for causing a computer to function as the presentation device according to any one of claims 1 to 5.

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