Teacher data creation device, teacher data creation method, and teacher data creation program

Abstract

To provide a teacher data creation device that can create teacher data with a consistent label regardless of the amount of teacher data having been given a label.SOLUTION: The present invention relates to a teacher data creation device for creating teacher data used for learning of a classifier for classifying data. The device includes: an output control unit for outputting second data similar to input first data and classification information provided to the second data; a reception unit for receiving classification information provided to the first data; and a teacher data creation unit for providing the received classification information to the first data and creating teacher data.SELECTED DRAWING: Figure 7

Description

[Technical field]

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

[0002] In recent years, there has been progress in the development of automated text classification for various applications, including spam filters for emails and filters for harmful text on web pages. In particular, the development of algorithms for text classification using machine learning has been promoted.

[0003] In relation to the above technology, the following prior art is disclosed in Patent Document 1 below: A first feature vector of reference data relating to a specific field is calculated. From the first feature vector, search conditions, such as a combination of search terms, used to collect data relating to the specific field are generated. Data relating to the specific field is collected from a document database based on the search conditions. A second feature vector of the collected data is calculated. The similarity between the second feature vector and the first feature vector is calculated. Then, from the collected data, data whose similarity falls within a predetermined range is extracted as training data to be used in machine learning.

[0004] Patent Document 2 also discloses the following prior art. A plurality of teacher images taught to one of a plurality of teaching classes are stored together with the teaching class. A set of teacher images selected from a plurality of teaching classes is set as a training image group, and a predetermined number of training image groups, which are mutually different sets, are selected. Each teacher image is classified into one of the plurality of teaching classes using a classifier constructed by learning using each training image group. Then, when the class with the highest classification rate of each class classified by each classifier using the predetermined number of training image groups in each teacher image is different from the teaching class of each teacher image, the teacher image is specified as a specific teacher image and displayed together with the classification rate of each class. As a result, the teaching class of the specific teacher image is corrected by the user based on the classification rate of each displayed class, so that the erroneous teaching class taught by the teacher image can be corrected. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2018-124617 A [Patent Document 2] JP 2017-162232 A Summary of the Invention [Problem to be solved by the invention]

[0006] However, the prior art described in Patent Document 1 has a problem in that it is not possible to create teacher data in which each sentence is consistently labeled. In addition, the prior art described in Patent Document 2 has a problem in that the accuracy of the teaching class taught to the teaching image depends on the accuracy of the classifier. There is also a problem in that a relatively large amount of teaching images, etc., required for training each classifier are required in advance.

[0007] The present invention has been made to solve such problems, and aims to provide a teacher data creation device, a teacher data creation method, and a teacher data creation program that are capable of creating teacher data with consistent labels regardless of the quantity of labeled teacher data. [Means for solving the problem]

[0008] The above-mentioned object of the present invention is achieved by the following means.

[0009] (1) A teacher data creation device for creating teacher data used to train a classifier that classifies data, the teacher data creation device having an output control unit that outputs second data similar to input first data and classification information assigned to the second data, a receiving unit that accepts the classification information to be assigned to the first data, and a teacher data creation unit that assigns the accepted classification information to the first data to create the teacher data.

[0010] (2) The teacher data creation device described in (1) above, wherein the output control unit further outputs a similarity of the second data to the first data together with the second data and the classification information.

[0011] (3) The teacher data creation device according to (1) or (2) above, wherein the first data and the second data are text data.

[0012] (4) The output control unit Special The teacher data creation device according to any one of (1) to (3) above, further comprising: a conversion unit that converts the first data into a feature vector; and an output unit that outputs the second data, the feature vector of which is similar to the feature vector into which the first data is converted, and the classification information assigned to the second data.

[0013] (5) The teacher data creation device described in (4) above, wherein the conversion unit converts the first data into the feature vector using a trained model generated by unsupervised learning using the data.

[0014] (6) A teacher data creation device described in any of (1) to (5) above, wherein the second data is data to which the classification information has been previously assigned by the teacher data creation unit, and which constitutes the teacher data together with the classification information.

[0015] (7) The teacher data creation device described in any of (1) to (6) above, wherein the output control unit further outputs the classification information previously accepted by the reception unit together with guidance information for accepting the classification information in the reception unit.

[0016] (8) The output control unit outputs the classification information previously received by the reception unit in a selectable manner, and the reception unit accepts the classification information selected from the classification information output by the output control unit in a selectable manner as the classification information to be assigned to the first data.

[0017] (9) The output control unit further outputs a selection object for enabling the acceptance of additional classification information other than the classification information previously accepted by the acceptance unit, and an input field for inputting the additional classification information, and the acceptance unit accepts the classification information entered into the input field when the selection object is selected as the classification information to be assigned to the first data, in the teacher data creation device described in (8) above.

[0018] (10) A method executed by a teacher data creation device,A teacher data creation method for creating teacher data used to train a classifier that classifies data, the method comprising the steps of: (a) outputting second data similar to input first data and classification information assigned to the second data; (b) accepting the classification information to be assigned to the first data; and (c) assigning the accepted classification information to the first data to create the teacher data.

[0019] (11) The teacher data creation method described in (10) above, wherein the step (a) further outputs a similarity of the second data to the first data together with the second data and the classification information.

[0020] (12) The teacher data creation method according to (10) or (11) above, wherein the first data and the second data are text data.

[0021] (13) In the step (a), the first data Special The method for creating teacher data according to any one of (10) to (12) above, comprising: a step (d) of converting the first data into a feature vector; and a step (e) of outputting the second data, the feature vector of which is similar to the feature vector into which the first data is converted, and the classification information assigned to the second data.

[0022] (14) The teacher data creation method described in (13) above, wherein in the step (d), the first data is converted into the feature vector by a trained model generated by unsupervised learning using the data.

[0023] (15) A teacher data creation method described in any one of (10) to (14) above, wherein the second data is data to which the classification information has been previously assigned in step (c), and thus constitutes the teacher data together with the classification information.

[0024] (16) A teacher data creation method according to any one of (10) to (15) above, wherein in the step (a), the classification information previously accepted in the step (b) is further output together with guidance information for accepting the classification information in the step (b).

[0025] (17) A method for creating teacher data according to any one of (10) to (16) above, wherein in the step (a), the classification information previously accepted in the step (b) is output in a selectable manner, and in the step (b), the classification information selected from the classification information output in the step (a) in a selectable manner is accepted as the classification information to be assigned to the first data.

[0026] (18) The teacher data creation method described in (17) above, wherein in the step (a), a selection object for enabling acceptance of additional classification information other than the classification information previously accepted in the step (b) and an input field for inputting the additional classification information are further output, and in the step (b), the classification information entered into the input field by selecting the selection object is accepted as the classification information to be assigned to the first data.

[0027] (19) A teacher data creation program for causing a computer to execute the teacher data creation method described in any one of (10) to (18) above. Effect of the Invention

[0028] The system outputs second data similar to the input first data and classification information assigned to the second data, accepts classification information to be assigned to the first data, and assigns the accepted classification information to the first data to create the teacher data. This makes it possible to create teacher data with consistent labels assigned regardless of the quantity of teacher data to which labels have been assigned. [Brief description of the drawings]

[0029] [Figure 1]FIG. 1 is a diagram showing the configuration of a teacher data creation system. [Diagram 2] FIG. 2 is a block diagram of a teacher data creation device. [Diagram 3] FIG. 2 is a functional block diagram of a control unit of the teacher data creation device. [Figure 4] 13 is a diagram showing an output screen output to an operation display unit by an output control unit. FIG. [Diagram 5] FIG. 2 is a block diagram showing the functions of a control unit when a classifier is trained using training data. [Figure 6] FIG. 2 is a block diagram showing the functions of a control unit during classification using a trained classifier trained using training data. [Figure 7] 1 is a flowchart showing the operation of the teacher data creation system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0030] Hereinafter, a teacher data creation device, a teacher data creation method, and a teacher data creation program according to an embodiment of the present invention will be described with reference to the drawings. In the drawings, the same elements are given the same reference numerals, and duplicated explanations will be omitted. Also, the dimensional ratios in the drawings are exaggerated for the convenience of explanation, and may differ from the actual ratios.

[0031] Fig. 1 is a diagram showing the configuration of a teacher data creation system 10. Fig. 2 is a block diagram of a teacher data creation device 100. The teacher data creation device 100 may be composed of multiple devices. For example, a part or all of the storage unit 120 may be composed of a cloud server that is virtually constructed by multiple servers arranged on a network such as the Internet.

[0032] The teacher data creation system 10 may include a teacher data creation device 100 and an input device 200.

[0033] The input device 200 is a device for inputting data (hereinafter also referred to as "input data") to which a correct class label (hereinafter simply referred to as "label") is assigned when creating teacher data (hereinafter simply referred to as "teacher data") used in machine learning, and is installed as necessary. For the sake of simplicity, the input data will be described as text data. The teacher data is created by assigning a label to the input data. That is, the teacher data is composed of a combination of the input data and a label. For the sake of convenience, in order to distinguish between teacher data to be created and existing teacher data that has already been created, the existing teacher data will be described below as a combination of "teacher text data" (corresponding to the input data) and a label. The existing teacher data may be teacher data that was created in the past by assigning a label accepted by the operation display unit 140 to the input data.

[0034] The training data is used for learning (machine learning) a classifier that classifies text data (data). The classifier may be, for example, a neural network model. Specifically, the classification of text data by the classifier corresponds to the output of a class into which the text data should be classified when the text data is input to the classifier.

[0035] The input device 200 may be omitted if input data can be acquired only by the teacher data creation device 100.

[0036] The input data may be text data (hereinafter, also referred to as "text data"). The input data may include text data, voice, sound, images (including images other than text images), etc. For simplicity of explanation, the following explanation will be given assuming that the input data is text data.

[0037] The input device 200 is a device capable of acquiring input data such as text data. The input device 200 may be configured, for example, by a device that converts text content included in HTML (HyperText Markup Language) data into text data (text data) or extracts text data from the content. The input device 200 may be configured by a scanner that reads out text included in a paper medium as an image, and a character conversion unit that converts the image into text data. The input device 200 may be configured by a microphone and a voice recognition device for converting voice into text data.

[0038] 2, the teacher data creation device 100 includes a control unit 110, a storage unit 120, a communication unit 130, and an operation display unit 140. These components are connected to each other via a bus 150. The teacher data creation device 100 is configured by, for example, a computer terminal.

[0039] The control unit 110 is configured with a CPU (Central Processing Unit) and memories such as a RAM (Random Access Memory) and a ROM (Read Only Memory), and controls each part of the teacher data creation device 100 and performs arithmetic processing according to a program. The functions of the control unit 110 will be described in detail later.

[0040] The storage unit 120 is configured with a hard disk drive (HDD), a solid state drive (SSD), etc., and stores various programs and various data.

[0041] The communication unit 130 is an interface circuit (for example, a LAN card) for communicating with an external device via a network.

[0042] The operation display unit 140 may be configured with, for example, a touch panel. The operation display unit 140 accepts various inputs from a user. The operation display unit 140 displays various information. As described below, the operation display unit 140 displays teacher sentence data similar to the input data and labels assigned to the teacher sentence data. The operation display unit 140 constitutes a reception unit. The labels constitute classification information.

[0043] The function of the control unit 110 will now be described.

[0044] Fig. 3 is a functional block diagram of the control unit 110. The control unit 110 functions as an acquisition unit 111, a conversion unit 112, a similarity calculation unit 113, an output control unit 114, and a teacher data creation unit 115. For ease of explanation, Fig. 3 also shows a storage unit 120 and an operation display unit 140. The output control unit 114 constitutes an output unit.

[0045] The acquisition unit 111 acquires the input data by receiving it from the input device 200. The acquisition unit 111 may acquire the input data by receiving it from an external device via the communication unit 130. When the input data is input on the operation display unit 140, the acquisition unit 111 may acquire the input data via the operation display unit 140. When the input data is stored in the storage unit 120, the acquisition unit 111 may acquire the input data by reading it out from the storage unit 120.

[0046] The conversion unit 112 converts the input data into a feature vector. The conversion unit 112 can convert the input data into a feature vector as follows. Below, an example will be described in which the input data is Japanese text data.

[0047] The conversion unit 112 performs word segmentation to separate the sentence, which is the input data, into words (nouns, verbs, adjectives, adverbs, particles, etc.). For example, the input data "I am a cat" is separated into the words "I," "is," "cat," "de," and "aru" by word segmentation. General-purpose technologies such as MeCab and Sudachi can be used for word segmentation.

[0048] Next, the conversion unit 112 performs distributed representation to convert each word into a vector. Through distributed representation, each word is converted into, for example, a 100-dimensional vector. Through distributed representation, a plurality of vectors corresponding to each word are calculated. For distributed representation, general-purpose technologies such as Word2Vec and fastText may be used. The conversion unit 112 may perform distributed representation using a trained model such as a neural network generated by unsupervised learning using texts available to the public in online interactive encyclopedias such as Wikipedia and SNS such as Twitter.

[0049] Next, the conversion unit 112 calculates one feature vector from the plurality of vectors that have been converted into a distributed representation. For example, the feature vector can be calculated as the average value, the sum, or the maximum value of the plurality of vectors.

[0050] The conversion unit 112 may be configured as a single module in which modules for performing word segmentation, distributed representation, and feature vector calculation are integrated. For example, the conversion unit 112 may be configured as a trained model generated by unsupervised learning using text data (data). The trained model may convert input data into a feature vector. The trained model may be a trained model such as a neural network generated by unsupervised learning using publicly available text.

[0051] The similarity calculation unit 113 calculates the similarity (hereinafter, simply referred to as "similarity") of the feature vector of each teacher text data included in the existing teacher data to the feature vector (feature vector of input data) calculated by the conversion unit 112. As described above, the existing teacher data is a combination of teacher text data and a label assigned to the teacher text data. The similarity can be calculated, for example, as the Euclidean distance or cosine distance between the feature vector calculated by the conversion unit 112 and each teacher text data included in the existing teacher data. The existing teacher data can be stored, for example, in the storage unit 120 in association with the feature vector of the teacher text data included in the teacher data. The similarity calculation unit 113 can use the existing teacher data and the feature vector associated with the existing teacher data by reading them from the storage unit 120.

[0052] The output control unit 114 extracts existing teacher data whose similarity calculated by the similarity calculation unit 113 is equal to or greater than a predetermined threshold value as specific teacher data from among the existing teacher data. The predetermined threshold value may be appropriately set by experiment or the like from the viewpoint of appropriately labeling the input data. The output control unit 114 may output the similarity between the input data, the specific teacher data, and the teacher sentence data included in the specific teacher data by displaying it on the operation display unit 140. Note that the output control unit 114 does not have to output the similarity and / or the input data. The output control unit 114 may output the similarity between the input data, the specific teacher data, and the teacher sentence data included in the specific teacher data by transmitting it to an external device or the like.

[0053] The output control unit 114 may further output the labels included in the existing teacher data together with guidance information (hereinafter simply referred to as "guidance information") for guiding the label to be assigned to the input data. The output control unit 114 may display the labels included in the existing teacher data selectably as the labels to be assigned to the input data. That is, the output control unit 114 may display the labels previously accepted as the labels to be assigned to the input data in the operation display unit 140 selectably. The output control unit 114 may further output a selection object 146 (see FIG. 4) for making it possible to accept labels other than the labels included in the existing teacher data as additional labels (additional classification information), and an input field 147 (see FIG. 4) for inputting the additional labels.

[0054] FIG. 4 is a diagram showing an output screen 141 that is output to the operation display unit by the output control unit 114. As shown in FIG.

[0055] In the example of the output screen 141 shown in FIG. 4, the input data is displayed (output) as "Eat spaghetti for lunch" in the input sentence column 142. In the output screen 141, the specific teacher data (teacher sentence data and label) and the similarity are displayed in the similar sentence column 143. In the similar sentence column, the teacher sentence data included in the specific teacher data are displayed in the "sentence" row, the label included in the specific teacher data are displayed in the "label" row, and the similarity is displayed in the "similarity" row. For example, the similarity of the teacher sentence data "Eat natto and rice for breakfast" to the input sentence "Eat spaghetti for lunch" is relatively high at 90%, and it can be seen that "Eat spaghetti for lunch" and "Eat natto and rice for breakfast" are determined to be particularly similar. Note that when teacher data is generated for the first time, when existing teacher data has not been generated and specific teacher data does not exist, the specific teacher data and the similarity are not displayed.

[0056] The output screen 141 displays the labels included in the existing teacher data together with the guidance information 144. The displayed labels may be all of the labels included in the existing teacher data. In the example of FIG. 4, "meal", "excretion", "sleep", and "exercise" are displayed as the labels included in the existing teacher data. The guidance information may be displayed on the label that is assigned to the teacher sentence data with the highest similarity. The guidance information may be displayed on the label that is the most numerous among the labels included in the specific teacher data. The labels included in the existing teacher data are displayed in the label assignment column 145. The labels included in the existing teacher data are displayed together with selectable check boxes, so that they can be selected. The user can select one of the labels by checking the check box with reference to the guidance information 144. The selected label is accepted as the label to be assigned to the input data.

[0057] The output screen 141 further displays a selection object 146 for allowing labels other than those included in the existing teacher data to be accepted as additional labels, and an input field 147 for inputting the additional label. The selection object 146 is composed of the word "add" and a selectable check box, and checking the check box allows the user to input an additional label into the input field 147. The additional label input into the input field 147 is accepted as a label to be assigned to the input data (hereinafter also referred to as an "assigned label").

[0058] A "previous sentence" button 148 and a "next sentence" button 149 may further be displayed on the output screen 141. When the "previous sentence" button 148 is selected, the input data (teacher sentence data) to which a label has been assigned immediately before is displayed in the "input sentence" field 142. This makes it possible to change the label assigned to the input data to which a label has been assigned immediately before. When the "next sentence" button 149 is selected, the input data next acquired by the acquisition unit 111 is displayed in the "input sentence" field 142. The teacher sentence data similar to the input data and the label assigned to the teacher sentence data are displayed together with the degree of similarity. In addition, labels and the like included in the existing teacher data are displayed.

[0059] The description will be continued with reference to Fig. 3 again. The teacher data creation unit 115 creates teacher data (new teacher data) by adding labels input by the user on the output screen 141 displayed on the operation display unit 140 to the input data.

[0060] The learning of the classifier using the created teacher data and the classification of text data by the trained classifier will be described below. For the sake of simplicity, the following description will be given taking as an example a case where the classifier 117 is a neural network model.

[0061] Fig. 5 is a block diagram showing functions of the control unit 110 when the classifier 117 uses training data. The functions of the control unit 110 shown in Fig. 5 can be realized by the control unit 110 executing a program.

[0062] The control unit 110 functions as a classifier 117 and a loss calculation unit 118. The classifier 117 is trained using teacher data. More specifically, the classifier 117 is trained using a combination of teacher text data and a label (teacher class) assigned to the teacher text data, which constitutes the teacher data. The loss calculation unit 118 calculates, as a class loss, a difference between a classification result (class) classified (estimated) by the classifier 117 when the teacher text data is input to the classifier 117 and a label assigned to the teacher text data. The classification result may be a likelihood for each class. The class loss may be calculated by subtracting the likelihood of the class of the label assigned to the teacher text data in the classification result by the classifier 117 from 1. The classifier 117 is trained by backpropagation so that the calculated class loss becomes small.

[0063] Fig. 6 is a block diagram showing the functions of the control unit 110 during classification (estimation) by the trained classifier 117 that has been trained using teacher data. The functions of the control unit 110 shown in Fig. 6 can be realized by the control unit 110 executing a program.

[0064] The control unit 110 may function as a trained classifier 117. The classifier 117 classifies text data by outputting a classification result (class) when text data to be classified is input. The classification result may be the likelihood for each class. As a result, the text data is classified into the class with the highest likelihood in the classification result.

[0065] 7 is a flowchart showing the operation of the teacher data creation system 10. This flowchart can be executed by the control unit 110 of the teacher data creation device 100 in accordance with a program.

[0066] The control unit 110 determines whether or not input data has been acquired (S101). If the control unit 110 determines that input data has not been acquired (S101: NO), it executes step S101 again.

[0067] When it is determined that input data has been acquired (S101: YES), the control unit 110 converts the input data into a feature vector (S102).

[0068] The control unit 110 reads out the existing teacher data and the feature vector of the teacher sentence data included in the existing teacher data from the storage unit 120 (S103).

[0069] The control unit 110 calculates a feature vector of the input data, and calculates the similarity of the feature vector of each piece of training text data to the feature vector of the input data (S104).

[0070] The control unit 110 extracts existing teacher data including teacher sentence data whose similarity is equal to or greater than a predetermined threshold as specific teacher data (S105).

[0071] The control unit 110 outputs the specific teacher data and the similarity (S106).

[0072] The control unit 110 outputs each label included in the existing teacher data by, for example, displaying it on the operation display unit 140 together with guidance information in a selectable manner (S107). The user can assign labels consistent with the specific teacher data by referring to the displayed specific teacher data and similarity, as well as each label and guidance information included in the existing teacher data.

[0073] The control unit 110 determines whether an assigned label to be assigned to the input data has been acquired by, for example, selecting one of the labels included in the existing teacher data displayed on the operation display unit 140 (S108). If the control unit 110 determines that an assigned label to be assigned to the input data has not been acquired (S108: NO), it executes step S108 again.

[0074] When the control unit 110 determines that the label to be assigned to the input data has been acquired (S108: YES), the control unit 110 assigns the label to the input data to create teacher data (new teacher data) (S109).

[0075] The embodiment provides the following advantages.

[0076] The second data similar to the input first data and the classification information assigned to the second data are output, the classification information to be assigned to the first data is accepted, and the accepted classification information is assigned to the first data to create the teacher data. This makes it possible to create teacher data with consistent labels assigned regardless of the amount of teacher data to which labels have been assigned. In addition, the more data classified as teacher data increases, the more second data similar to the first data increases, thereby improving the consistency of the teacher data. In addition, since second data similar to the first data is output, human error in labeling can be suppressed.

[0077] Furthermore, the similarity of the second data to the first data is output together with the second data and the classification information, thereby improving the accuracy of the training data.

[0078] Furthermore, the first data and the second data are text data, which makes it possible to more effectively create training data that is consistently labeled.

[0079] Furthermore, the system converts the first data into a feature vector, and outputs second data whose feature vector is similar to the feature vector into which the first data was converted, as well as classification information added to the second data. This makes it easier to create highly accurate and consistent training data.

[0080] In addition, the first data is converted into a feature vector using a trained model generated by unsupervised learning, making it easier and less costly to create consistent training data.

[0081] Furthermore, the second data is data that constitutes teacher data together with the classification information, as a result of having been previously assigned classification information by the teacher data creation unit, thereby making it possible to create consistent teacher data more simply and effectively.

[0082] Furthermore, previously accepted classification information is further output together with guidance information for accepting more classification information, thereby making it possible to assign classification information to the input first data more easily and in a shorter time.

[0083] Furthermore, previously accepted classification information is output in a selectable manner, and selected classification information from the selectable output classification information is accepted as classification information to be assigned to the first data. This makes it possible to assign labels more easily and to reduce human error in label assignment.

[0084] Furthermore, a selection object for enabling reception of additional classification information other than the classification information previously received and an input field for inputting the additional classification information are further output, and the classification information inputted into the input field after the selection object is selected is received as the classification information to be assigned to the first data. This allows for more flexible label assignment.

[0085] The teacher data creation device, teacher data creation method, and teacher data creation program described above are merely examples of the main configurations used to explain the features of the above-mentioned embodiments, and are not limited to the above-mentioned configurations and may be modified in various ways within the scope of the claims. Furthermore, configurations used in general teacher data creation, etc. are not excluded.

[0086] For example, some steps may be omitted from the above-described flowchart, other steps may be added, some of the steps may be executed simultaneously, or one step may be divided into multiple steps and executed.

[0087] In addition, the means and methods for performing various processes in the above-mentioned system can be realized by either a dedicated hardware circuit or a programmed computer. The above-mentioned program may be provided by a computer-readable recording medium such as a USB memory or a DVD (Digital Versatile Disc)-ROM, or may be provided online via a network such as the Internet. In this case, the program recorded on the computer-readable recording medium is usually transferred to and stored in a storage unit such as a hard disk. In addition, the above-mentioned program may be provided as a standalone application software, or may be incorporated as a function into the software of a device such as a teacher data creation device. [Explanation of symbols]

[0088] 10. Teacher data creation system, 100 Teacher data creation device, 110 control section, 111 Acquisition Department; 112 conversion unit, 113 Similarity calculation unit, 114 Output control section, 115 Teacher Data Creation Department, 117 classifier, 118 Loss calculation section, 120 storage section, 130 Communications Department, 140 Operation display section, 200 input device, 201 images, 202 objects of analysis.

Claims

1. A training data creation device for creating training data used for training a classifier that classifies data, comprising: an output control unit that outputs second data similar to the input first data and classification information assigned to the second data; a reception unit that receives the classification information to be assigned to the first data; a teacher data creation unit that creates the teacher data by adding the received classification information to the first data; A teacher data creation device having the above structure.

2. The teacher data generating device according to claim 1 , wherein the output control unit further outputs a similarity of the second data to the first data together with the second data and the classification information.

3. The teacher data generating device according to claim 1 , wherein the first data and the second data are text data.

4. The output control unit is A conversion unit that converts the first data into a feature vector; The teacher data creation device according to any one of claims 1 to 3, further comprising: an output unit that outputs the second data whose feature vector is similar to the feature vector into which the first data is converted, and the classification information assigned to the second data.

5. The teacher data creation device according to claim 4 , wherein the conversion unit converts the first data into the feature vector using a trained model generated by unsupervised learning using the data.

6. The teacher data creation device according to any one of claims 1 to 5, wherein the second data is data to which the classification information has been previously assigned by the teacher data creation unit, and which constitutes the teacher data together with the classification information.

7. The teacher data creation device according to any one of claims 1 to 6, wherein the output control unit further outputs the classification information previously accepted by the acceptance unit together with guidance information for accepting the classification information in the acceptance unit.

8. The output control unit outputs the classification information previously accepted by the acceptance unit in a selectable manner; The teacher data creation device according to any one of claims 1 to 7, wherein the reception unit receives the selected classification information from the classification information selectably output by the output control unit as the classification information to be assigned to the first data.

9. the output control unit further outputs a selection object for enabling reception of additional classification information other than the classification information previously received by the reception unit, and an input field for inputting the additional classification information; The teacher data creation device according to claim 8 , wherein the reception unit receives the classification information inputted into the input field when the selected object is selected as the classification information to be assigned to the first data.

10. A method executed by a teacher data creation device, comprising: A method for creating training data for use in training a classifier that classifies data, comprising the steps of: (a) outputting second data similar to the input first data and classification information assigned to the second data; (b) receiving the classification information to be assigned to the first data; (c) adding the received classification information to the first data to create the teacher data; A method for creating teacher data comprising the steps of:

11. 11. The teacher data generating method according to claim 10, wherein said step (a) further outputs a similarity of said second data to said first data together with said second data and said classification information.

12. The teacher data generating method according to claim 10 or 11, wherein the first data and the second data are text data.

13. The step (a) comprises: (d) converting the first data into a feature vector; The teacher data creation method according to any one of claims 10 to 12, further comprising a step (e) of outputting the second data whose feature vector is similar to the feature vector into which the first data is converted, and the classification information assigned to the second data.

14. 14. The teacher data generating method according to claim 13, wherein in the step (d), the first data is converted into the feature vector by a trained model generated by unsupervised learning using the data.

15. The teacher data creation method according to any one of claims 10 to 14, wherein the second data is data to which the classification information has been previously assigned in step (c) and which, together with the classification information, constitutes the teacher data.

16. The teacher data creation method according to any one of claims 10 to 15, wherein in the step (a), the classification information previously accepted in the step (b) is further output together with guidance information for accepting the classification information in the step (b).

17. In the step (a), the classification information previously received in the step (b) is output in a selectable manner; The teacher data creation method according to any one of claims 10 to 16, wherein the step (b) accepts the selected classification information from the classification information output in a selectable manner in the step (a) as the classification information to be assigned to the first data.

18. In the step (a), a selection object for enabling reception of additional classification information other than the classification information previously received in the step (b) and an input field for inputting the additional classification information are further output; 18. The teacher data creation method according to claim 17, wherein in the step (b), the classification information entered in the input field when the selected object is selected is accepted as the classification information to be assigned to the first data.

19. A teacher data creation program for causing a computer to execute the teacher data creation method according to any one of claims 10 to 18.

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