Domain-specific method for distinguishing type-denoting domain terms from entity-denoting domain terms

• US 10,073,831 B1
• Filed: 03/09/2017
• Issued: 09/11/2018
• Est. Priority Date: 03/09/2017
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Beller, Charles E., Chase, Jr., Paul J., Darden, Richard L., Drzewucki, Michael, Katz, Edward G., Phipps, Christopher, Ramirez, James E.
• Primary Examiner(s): Yen, Eric
• Application Number:
  US15/454,778
• Publication Number:
  US 20180260382A1
• Time in Patent Office: 551 Days
• 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 16/35 Clustering; Classification
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• 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 40/35 Discourse or dialogue representation

Abstract

Large lists of domain-specific terms are classified as a particular kind of linguistic object, e.g., lexical answer type T versus canonical answer E, based on features from a domain-specific corpus which have been found to distinguish between the linguistic objects. The distinguishing features can be identified in the corpus based on sets of the linguistic objects derived from question-and-answer pairs. A classifier can be trained using the distinguishing features, and the classification carried out using that classifier. The distinguishing features can include one or more syntactic features or one or more lexical features. The linguistic objects (the T and E training sets) can be extracted from the question-and-answer pairs automatically via text analysis if manually curated lists are not available. The classified terms can be included in a domain-specific lexicon which facilitates a deep question answering system to yield an answer to a question.

Claims

1. A computer system comprising:

one or more processors which process program instructions;

a memory device connected to said one or more processors; and

program instructions residing in said memory device for distinguishing domain-specific terms from a list specific to a particular domain by extracting linguistic objects from a set of question-and-answer pairs wherein the linguistic objects include lexical answer types and answer entities, grouping the lexical answer types into a first set and grouping the answer entities into a second set, identifying distinguishing features of one or more corpora specific to a particular domain wherein the distinguishing features distinguish the lexical answer types in the first set from the answer entities in the second set, and classifying the domain-specific terms as either lexical answer type or answer entity based on the distinguishing features,

wherein said program instructions further train a natural language classifier using the distinguishing features, and the classifying uses the natural language classifier.

2. The computer system of claim 1 wherein the distinguishing features include one or more syntactic features.

3. The computer system of claim 1 wherein the distinguishing features include one or more lexical features.

4. The computer system of claim 1 wherein the extracting uses text analysis to automatically extract the linguistic objects.

5. The computer system of claim 1 wherein said program instructions further apply a lexicon of classified terms to a deep question answering system to yield an answer to a question.

6. A computer program product comprising:

a computer readable storage medium; and

program instructions residing in said storage medium for distinguishing domain-specific terms from a list specific to a particular domain by extracting linguistic objects from a set of question-and-answer pairs wherein the linguistic objects include lexical answer types and answer entities, grouping the lexical answer types into a first set and grouping the answer entities into a second set, identifying distinguishing features of one or more corpora specific to a particular domain wherein the distinguishing features distinguish the lexical answer types in the first set from the answer entities in the second set, and classifying the domain-specific terms as either lexical answer type or answer entity based on the distinguishing features,

wherein said program instructions further train a natural language classifier using the distinguishing features, and the classifying uses the natural language classifier.

7. The computer program product of claim 6 wherein the distinguishing features include one or more syntactic features.

8. The computer program product of claim 6 wherein the distinguishing features include one or more lexical features.

9. The computer program product of claim 6 wherein the extracting uses text analysis to automatically extract the sets of linguistic objects.