ANNOTATED TEXT DATA EXPANDING METHOD, ANNOTATED TEXT DATA EXPANDING COMPUTER-READABLE STORAGE MEDIUM, ANNOTATED TEXT DATA EXPANDING DEVICE, AND TEXT CLASSIFICATION MODEL TRAINING METHOD

• US 20190317986A1
• Filed: 04/12/2019
• Published: 10/17/2019
• Est. Priority Date: 04/13/2018
• Status: Abandoned Application

• Current Assignee: Preferred Networks, Inc.
• Original Assignee: Preferred Networks, Inc.
• Inventors: KOBAYASHI, Sosuke
• Application Number:
  US16/383,065
• Publication Number:
  US 20190317986A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 40/169 Annotation, e.g. comment data or footnotes
• CPC Class Codes: G06F 40/232 Orthographic correction, e.g. spell checking or vowelisation
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods

Abstract

The present disclosure provides an annotated text data expanding method capable of obtaining a large amount of annotated text data, which is not inconsistent with an annotation label and is not unnatural as a text, by mechanically expanding a small amount of annotated text data through a natural language processing. The annotated text data expanding method includes inputting, by an input device, the annotated text data including a first text appended with a first annotation label to a prediction complementary model. New annotated text data is created by one or more processors by the prediction complementary model, with reference to the first annotation label and context of the first text.

Claims

1. A method for expanding annotated text data, the method comprising:

inputting, by an input device, the annotated text data including a first text appended with a first annotation label to a prediction complementary model; and

creating, by one or more processors, new annotated text data by the prediction complementary model, with reference to the first annotation label and context of the first text.

2. The method according to claim 1, wherein the creating new annotated text data comprises:

extracting, by the one or more processors, a candidate element replaceable with an element within the first text, by an extraction method provided in the prediction complementary model;

creating, by the one or more processors, a second text by replacing the element within the first text with the candidate element; and

appending, by the one or more processors, the first annotation label to the second text.

3. The method according to claim 1, wherein the prediction complementary model is a label-conditioned bidirectional language model.

4. The method according to claim 2, wherein the extraction method comprises calculating a probability distribution by a following equation (1):

p_τ

(·

|y,S\{w_i}) 

 

(1)

wherein τ

represents a temperature parameter, y represents an annotation label, S represents a text, and w_i represents an element in a text.

5. The method according to claim 2, wherein the element within the first text is a word.

6. The method according to claim 1, further comprising:

training, by the one or more processors prior to the inputting annotated text data, the prediction complementary model by using a text data set having no label as training data.

7. The method according to claim 1, wherein the first annotation label is one of (1) a positive annotation label indicating that text data has a positive meaning or (2) a negative annotation label indicating that text data has a negative meaning.

16. A method for training a text classification model, the method comprising using an expanded data set obtained by the annotated text data expanding method according to claim 1 as training data for a text classification model.

17. The method according to claim 16, wherein the creating new annotated text data comprises:

extracting, by the one or more processors, a candidate element replaceable with an element within the first text, by an extraction method provided in the prediction complementary model;

creating, by the one or more processors, a second text by replacing the element within the first text with the candidate element; and

appending, by the one or more processors, the first annotation label to the second text.

18. The method according to claim 16, wherein the prediction complementary model is a label-conditioned bidirectional language model.

19. The method according to claim 17, wherein the extraction method comprises calculating a probability distribution by a following equation (1):

p_τ

(·

|y,S\{w_i}) 

 

(1)

wherein τ

represents a temperature parameter, y represents an annotation label, S represents a text, and w_i represents an element in a text.

8. A non-transitory computer readable storage medium storing a program for expanding annotated text data, the program, when executed by one or more processors, causing the one or more processors to perform an expansion of the annotated text data,

wherein the program causes the one or more processors to:

input the annotated text data including a first text appended with a first annotation label to a prediction complementary model; and

create new annotated text data by the prediction complementary model, with reference to the first annotation label and context of the first text.

9. A device for expanding annotated text data, the device comprising:

a storage configured to store a prediction complementary model;

an input device configured to input the annotated text data including a first text appended with a first annotation label; and

one or more processors coupled to the storage and the input device and configured to perform arithmetic processings of creating new annotated text data by the prediction complementary model, with reference to the first annotation label and context of the first text.

10. The device according to claim 9, wherein the one or more processors are further configured to:

extract a candidate element replaceable with an element within the first text, by an extraction method provided in the prediction complementary model;

create a second text by replacing the element within the first text with the candidate element; and

append an annotation label identical to the first annotation label, to the second text.

11. The device according to claim 9 wherein the prediction complementary model is a label-conditioned bidirectional language model.

12. The device according to claim 10, wherein the extraction method comprises calculating a probability distribution by a following equation (1):

p_τ

(·

|y,S\{w_i}) 

 

(1)

wherein τ

represents a temperature parameter, y represents an annotation label, S represents a text, and w_i represents an element in a text.

13. The device according to claim 10, wherein the element within the first text is a word.

14. The device according to claim 9, wherein the one or more processors are further configured to:

train, prior to the inputting annotated text data, the prediction complementary model by using a text data set having no label as training data.

15. The device according to claim 9, wherein the first annotation label is one of (1) a positive annotation label indicating that text data has a positive meaning or (2) a negative annotation label indicating that text data has a negative meaning.