DEVICE, METHOD, AND PROGRAM FOR WORD SENSE ESTIMATION

• US 20150006155A1
• Filed: 03/07/2012
• Published: 01/01/2015
• Est. Priority Date: 03/07/2012
• Status: Abandoned Application

• Current Assignee: Mitsubishi Electric Corporation
• Original Assignee: Mitsubishi Electric Corporation
• Inventors: Tanigaki, Koichi, Takayama, Shigenobu, Shiba, Mitsuteru
• Application Number:
  US14/366,066
• Publication Number:
  US 20150006155A1
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 40/237 Lexical tools
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30

Abstract

A device and method to estimate a word sense with high accuracy by unsupervised learning. A word sense estimation device executes a plurality of number of times a probability calculation of calculating an evaluation value for each word of a case where each concept extracted as a word sense candidate is determined as a word sense, based on a proximity between a context feature of a selected word and a context feature of another word, a proximity between a selected concept and a word sense of this another word, and a probability that the selected word takes a selected word sense, and of re-calculating the probability based on the evaluation value calculated, and estimates a concept with a higher probability calculated of said each word, to be a word sense of the word.

Claims

1. A word sense estimation device comprising:

a word extraction part which extracts a plurality of words included in input data;

a context analysis part which extracts, for each word extracted by the word extraction part, a context feature of a context in which the word appears in the input data;

a word sense candidate extraction part which extracts each concept stored as a word sense of said each word, as a word sense candidate of said each word, from a concept dictionary storing at least one concept as a word sense of a word; and

a word sense estimation part which executes a plurality of number of times a probability calculation of calculating an evaluation value for said each word of a case where said each concept extracted as the word sense candidate by the word sense candidate extraction part is determined as a word sense, based on a proximity between the context feature of a selected word and the context feature of another word, a proximity between a selected concept and a concept of a word sense candidate of said another word, and a probability that the selected word takes a selected word sense, and of re-calculating the probability based on the evaluation value calculated, and which estimates a concept with a higher probability calculated of said each word, to be a word sense of the word.

2. The word sense estimation device according to claim 1,

wherein the word sense estimation part calculates the evaluation value such that:

the closer the context features to each other, the higher the evaluation value;

the closer the selected concept and a word sense of said another word to each other, the higher the evaluation value; and

the higher the probability, the higher the evaluation value, and re-calculates the probability such that the higher the evaluation value calculated, the higher the probability.

3. The word sense estimation device according to claim 2,

wherein the word sense estimation part calculates a joint probability p(x, s) as an evaluation value, assuming that x is the selected word and s is the selected concept, by Formula 1:

4. The word sense estimation device according to claim 3,

wherein the word sense estimation part calculates a probability π

^w_s that the word x takes the concept s, by Formula 2;

5. The word sense estimation device according to claim 4,

wherein the word sense estimation part calculates a total likelihood L in the probability calculation by Formula 3, repeatedly until an increment of a total likelihood L calculated in an (n+1)-th probability calculation, n being an integer of 1 or more, with respect to a total likelihood L calculated in an n-th probability calculation becomes less than a predetermined threshold θ

;

6. The word sense estimation device according to claim 5,

wherein the word sense estimation part, for said each word, substitutes 1 for the probability π

^w_s, being highest, of a word sense candidate, the probability π

^w_s being calculated by Formula 2, and 0 for the probability π

^w_s of another word sense candidate, calculates the total likelihood L, and re-calculates the evaluation value.

7. The word sense estimation device according to claim 1,

wherein the context feature includes at least either one of a neighboring word of the selected word and a word included in another character string associated to a character string including the selected word.

8. The word sense estimation device according to claim 1,

wherein the context feature includes at least either one of a word sense of a neighboring word of the selected word and a word sense of a word included in another character string associated to a character string including the selected word.

9. The word sense estimation device according to claim 1,

wherein a concept stored in the concept dictionary as a word sense of a word is set with a hierarchical relation expressed by a graph structure, and a proximity between two concepts is determined by the number of links between the concepts.

10. The word sense estimation device according to claim 1,

wherein, in a case where a word extracted by the word extraction part is not registered in the concept dictionary, the word sense candidate extraction part specifies, from the concept dictionary, a word having a similarity of at least a predetermined degree with respect to a character string that constitutes the word, and extracts each concept stored as a word sense for the word specified, as a word sense candidate for the word extracted by the word sense candidate extraction part.

11. The word sense estimation device according to claim 1,

wherein, in a case where a word sense of a certain word is given in advance, the word sense estimation part fixes the probability of a word sense candidate corresponding to the given word sense among word sense candidates to 1, and fixes the probabilities of remaining word sense candidates to 0.

12. A word sense estimation method comprising:

a word extraction step of, with a processing device, extracting a plurality of words included in input data;

a context analysis step of, with the processing device, extracting, for each word extracted in the word extraction step, a context feature of a context in which the word appears in the input data;

a word sense candidate extraction step of, with the processing device, extracting each concept stored as a word sense of said each word, as a word sense candidate of said each word, from a concept dictionary storing at least one concept as a word sense of a word; and

a word sense estimation step of, with the processing device;

executing a plurality of number of times a probability calculation of calculating an evaluation value for said each word of a case where each concept extracted as the word sense candidate in the word sense candidate extraction step is determined as a word sense, based on a proximity between the context feature of a selected word and the context feature of another word, a proximity between a selected concept and a concept of a word sense candidate of said another word, and a probability that the selected word takes a selected word sense, and of re-calculating the probability based on the evaluation value calculated; and

estimating a concept with a higher probability calculated of said each word, to be a word sense of the word.

13. A word sense estimation program adapted to cause a computer to execute:

a word extraction process of extracting a plurality of words included in input data;

a context analysis process of extracting, for each word extracted in the word extraction process, a context feature of a context in which the word appears in the input data;

a word sense candidate extraction process of extracting each concept stored as a word sense of said each word, as a word sense candidate of said each word, from a concept dictionary storing at least one concept as a word sense of a word; and

a word sense estimation process of;

executing a plurality of number of times a probability calculation of calculating an evaluation value for said each word of a case where each concept extracted as the word sense candidate in the word sense candidate extraction process is determined as a word sense, based on a proximity between the context feature of a selected word and the context feature of another word, a proximity between a selected concept and a concept of a word sense candidate of said another word, and a probability that the selected word takes a selected word sense, and of re-calculating the probability based on the evaluation value calculated; and

estimating a concept with a higher probability calculated of said each word, to be a word sense of the word.