INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD AND MEDIUM

• US 20150278194A1
• Filed: 11/07/2013
• Published: 10/01/2015
• Est. Priority Date: 11/07/2012
• Status: Abandoned Application

• Current Assignee: NEC Corporation
• Original Assignee: NEC Corporation
• Inventors: Terao, Makoto, Koshinaka, Takafumi
• Application Number:
  US14/440,931
• Publication Number:
  US 20150278194A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/279 Recognition of textual entities
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

An information processing device according to the present invention includes: a global context extraction unit which identifies a word, a character, or a word string included in data as a specific word, and extracts a set of words included in at least a predetermined range extending from the specific word as a global context; a context classification unit which classifies the global context based on a predetermined viewpoint, and outputs a result of classification; and a language model generation unit which generates a language model for calculating a generation probability of the specific word by using the result of the classification.

Claims

1. An information processing device comprising:

a global context extraction unit which identifies a word, a character, or a word string included in data as a specific word, and extracts a set of words included in at least a predetermined range extending from the specific word as a global context;

a context classification unit which classifies the global context based on a predetermined viewpoint, and outputs a result of classification; and

a language model generation unit which generates a language model for calculating a generation probability of the specific word by using the result of the classification.

2. The information processing device according to claim 1, comprising:

a context classification model generation unit which generates a context classification model for indicating a relationship between the set of words and a class based on the predetermined viewpoint based on predetermined language data, wherein

the context classification unit classifies the global context by using the context classification model.

3. The information processing device according to claim 2, wherein

the context classification model generation unit generates a model for calculating a posterior probability of a class when a set of words are given by making a plurality of sets of words given class information training data.

4. The information processing device according to claim 2, wherein

the language model generation unit uses a maximum entropy model by making a posterior probability of the class a feature function.

5. The information processing device according to claim 1, comprising:

trigger feature calculation unit which calculates a feature function for a trigger pair between a word included in the global context and the specific word, wherein

the language model generation unit generates a language model by using the result of the classification and the feature function for the trigger pair.

6. The information processing device according to claim 1, comprising:

feature function calculation unit which calculates a feature function for an N-gram immediately preceding the specific word, wherein

the language model generation unit generates a language model by using the result of the classification and the feature function for the N-gram.

7. The information processing device according to claim 1, comprising:

trigger feature calculation unit which calculates a feature function for a trigger pair between a word included in the global context and the specific word; and

feature function calculation unit which calculates a feature function for an N-gram immediately preceding the specific word, wherein

the language model generation unit generates a language model by using the result of the classification, the feature function for the trigger pair, and the feature function for the N-gram.

8. An information processing method comprising:

identifying a word, a character, or a word string included in data as a specific word, and extracting a set of words included in at least a predetermined range extending from the specific word as a global context;

classifying the global context based on a predetermined viewpoint, and outputting a result of classification; and

generating a language model for calculating a generation probability of the specific word by using the result of the classification.

9. The information processing method according to claim 8, comprising:

generating a context classification model for indicating a relationship between the set of words and a class based on the predetermined viewpoint based on predetermined language data; and

classifying the global context by using the context classification model.

10. The information processing method according to claim 9, comprising:

generating a model for calculating a posterior probability of a class when a set of words are given by making a plurality of sets of words given class information training data.

11. The information processing method according to claim 9, comprising:

using a maximum entropy model by making a posterior probability of the class a feature function.

12. The information processing method according to claim 8, comprising:

calculating a feature function for a trigger pair between a word included in the global context and the specific word; and

generating a language model by using the result of the classification and the feature function for the trigger pair.

13. The information processing method according to claim 8, comprising:

calculating a feature function for an N-gram immediately preceding the specific word; and

generating a language model by using the result of the classification and the feature function for the N-gram.

14. The information processing method according to claim 8, comprising:

calculating a feature function for a trigger pair between a word included in the global context and the specific word;

calculating a feature function for an N-gram immediately preceding the specific word; and

generating a language model by using the result of the classification, the feature function for the trigger pair, and the feature function for the N-gram.

15. A computer readable non-transitory medium embodying a program, the program causing a computer to perform a method, the method comprising:

identifying a word, a character, or a word string included in data as a specific word, and extracting a set of words included in at least a predetermined range extending from the specific word as a global context;

classifying the global context based on a predetermined viewpoint and outputting a result of classification; and

generating a language model for calculating a generation probability of the specific word by using the result of the classification.

16. The method according to claim 15, comprising:

generating a context classification model for indicating a relationship between the set of words and a class based on the predetermined viewpoint based on a predetermined language data; and

classifying the global context by using the context classification model.

17. The method according to claim 16, comprising:

calculating a posterior probability of a class when a set of words are given by making a plurality of sets of words given class information training data.

18. The method according to claim 15, comprising:

using a maximum entropy model by making a posterior probability of the class a feature function.

19. The method according to claim 15, comprising:

calculating a feature function for a trigger pair between a word included in the global context and the specific word; and

generating a language model by using the result of the classification and the feature function for the trigger pair.

20. The according to claim 15, comprising:

calculating a feature function for an N-gram immediately preceding the specific word; and

generating a language model by using the result of the classification and the feature function for the N-gram.

21. (canceled)