QUERY DISAMBIGUATION IN A QUESTION-ANSWERING ENVIRONMENT

• US 20160259863A1
• Filed: 09/24/2015
• Published: 09/08/2016
• Est. Priority Date: 03/02/2015
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Kohler, Claire L., Allen, Corville O.
• Granted Patent: US 10,169,490 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/90332 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars

Abstract

Query disambiguation can include identifying, a first set of word clusters in a first candidate answer generated in response to a first input query for a question-answering system. Each of the first set of word clusters can be classified according to a syntactic element of a first set of syntactic elements. Query disambiguation can include identifying, in the first input query, a second set of word clusters. Each of the second set of word clusters can be classified according to a syntactic element of a second set of syntactic elements. Query disambiguation can include determining that the first word cluster is classified according to a first syntactic element that is not present in the second set of syntactic elements and generating a second input query using the first input query and the first word cluster.

Claims

1. A computer-implemented method of query disambiguation in a question-answering environment, the method comprising:

identifying, using a first natural language processing technique, a first set of word clusters in a first candidate answer generated in response to a first input query for a question-answering system, the first set of word clusters including a first word cluster, each of the first set of word clusters classified according to a syntactic element of a first set of syntactic elements;

identifying, using the first natural language processing technique, in the first input query, a second set of word clusters, each of the second set of word clusters classified according to a syntactic element of a second set of syntactic elements;

determining, by comparing the first and second sets of syntactic elements, that the first word cluster is classified according to a first syntactic element that is not present in the second set of syntactic elements; and

generating, using a second natural language processing technique, a second input query using the first input query and the first word cluster.

2. The method of claim 1, wherein the first word cluster is classified according to a second syntactic element and a second word cluster of the second set of word clusters is classified according to a third syntactic element, and wherein the method further comprises:

determining that the first syntactic element and the second syntactic element achieve a similarity criterion; and

generating, by replacing the second word cluster with the first word cluster, a third input query, the third input query generated in response to determining that the first and second syntactic elements achieve the similarity criterion.

3. The method of claim 1, wherein:

the syntactic elements of the first set of syntactic elements are parts of speech of a first set of parts of speech and the syntactic elements of the second set of syntactic elements are parts of speech of a second set of parts of speech.

4. The method of claim 1, wherein the first natural language processing technique and second natural language processing technique include using a slot grammar parser.

5. The method of claim 1, further comprising:

determining, using a third natural language processing technique, a set of domains from the first set of word clusters, each of the set of domains corresponding to a topic;

determining a number of domains in the set of domains;

determining a confidence value for the first candidate answer; and

wherein the determining that the first word cluster is classified according to a syntactic element not present in the second set of syntactic elements, and the generating the second input query are executed in response to determining that the confidence value achieves a first ambiguity criterion and the number of domains achieves a second ambiguity criterion.

6. The method of claim 1, wherein the second input query includes a plurality of word clusters arranged in a first order, and wherein the method further comprises:

determining, based the first order of the plurality of word clusters, a set of input query attributes for the second input query;

determining, by comparing the set of input query attributes to a set of input query criteria, that the set of input query attributes does not satisfy the set of input query criteria; and

generating, in response to determining that the set of input query attributes does not satisfy the set of input query criteria, a third input query including the plurality of word clusters arranged in a second order.

7. The method of claim 6, wherein the second input query including the plurality of word clusters arranged in the first order is structured to have a passive voice and the third input query including the plurality of word clusters arranged in the second order is structured to have an active voice.

8. The method of claim 1, further comprising:

displaying, using a graphical user interface, the second input query;

identifying, using a visual emphasis in the graphical user interface, the first word cluster;

toggling, between the second input query and the first input query, in response to receiving one or more user inputs via the graphical user interface, the one or more user inputs corresponding to the visual emphasis of the first word cluster.

9. The method of claim 1, further comprising:

receiving the first input query for the question-answering system;

analyzing, using a natural language processing technique, a corpus;

identifying, based on analyzing the corpus, a set of candidate answers for the first input query, the set of candidate answers including the first candidate answer.