Utilizing Word Embeddings for Term Matching in Question Answering Systems

• US 20160358094A1
• Filed: 06/02/2015
• Published: 12/08/2016
• Est. Priority Date: 06/02/2015
• Status: Active Grant

• Current Assignee: Workday Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: Fan, James J., Wang, Chang, Xiang, Bing, Zhou, Bowen
• Granted Patent: US 10,467,268 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

Software that compares vector representations of question terms and passage terms in question answering systems by performing the following steps: (i) receiving a question; (ii) generating a plurality of vectors including a first vector representation of a term in the question and a second vector representation of a term in a set of natural language text; (iii) generating a similarity score representing an amount of similarity between the first vector representation and the second vector representation; and (iv) determining whether the set of natural language text is relevant to the question based, at least in part, on the generated similarity score.

Claims

1-9. -9. (canceled)

10. A computer program product comprising a computer readable storage medium having stored thereon:

first program instructions programmed to receive a question;

second program instructions programmed to generate a plurality of vectors including a first vector representation of a term in the question and a second vector representation of a term in a set of natural language text;

third program instructions programmed to generate a similarity score representing an amount of similarity between the first vector representation and the second vector representation; and

fourth program instructions programmed to determine whether the set of natural language text is relevant to the question based, at least in part, on the generated similarity score.

11. The computer program product of claim 10, wherein the generating of the similarity score utilizes unsupervised learning method(s).

12. The computer program product of claim 11, wherein the unsupervised learning method(s) include comparing the first vector representation and the second vector representation using a similarity function.

13. The computer program product of claim 12, wherein the similarity function is at least one of a cosine similarity function and a Euclidean distance function.

14. The computer program product of claim 10, wherein the generating of the similarity score utilizes supervised learning method(s).

15. The computer program product of claim 14, wherein the supervised learning method(s) include utilizing an artificial neural network.

16. A computer system comprising:

a processor(s) set; and

a computer readable storage medium;

wherein;

the processor set is structured, located, connected and/or programmed to run program instructions stored on the computer readable storage medium; and

the program instructions include;

first program instructions programmed to receive a question;

second program instructions programmed to generate a plurality of vectors including a first vector representation of a term in the question and a second vector representation of a term in a set of natural language text;

third program instructions programmed to generate a similarity score representing an amount of similarity between the first vector representation and the second vector representation; and

fourth program instructions programmed to determine whether the set of natural language text is relevant to the question based, at least in part, on the generated similarity score.

17. The computer system of claim 16, wherein the generating of the similarity score utilizes unsupervised learning method(s).

18. The computer system of claim 17, wherein:

the unsupervised learning method(s) include comparing the first vector representation and the second vector representation using a similarity function; and

the similarity function is at least one of a cosine similarity function and a Euclidean distance function.

19. The computer system of claim 16, wherein the generating of the similarity score utilizes supervised learning method(s).

20. The computer system of claim 19, wherein the supervised learning method(s) include utilizing an artificial neural network.