KNOWLEDGE-RICH AUTOMATIC TERM DISAMBIGUATION

• US 20150039290A1
• Filed: 08/01/2013
• Published: 02/05/2015
• Est. Priority Date: 08/01/2013
• Status: Active Grant

• Current Assignee: AirBNB Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: ALEXE, Bogdan, LI, Yunyao, BALDWIN, Tyler Shore, STANOI, Ioana Roxana, VAITHYANATHAN, Shivakumar
• Granted Patent: US 9,633,009 B2
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 40/247 Thesauruses; Synonyms
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates

Abstract

Embodiments of the invention relate to ambiguity detection. In one embodiment, an object and a topical domain associated with the object are obtained. In this embodiment, the object includes at least one term. At least one of a plurality of information sources is analyzed based on the at least one term and the topical domain. A determination is made that object is one of ambiguous and unambiguous based on analyzing at least one of the plurality of information sources.

Claims

1. A method, comprising:

obtaining an object and a topical domain associated with the object, wherein the object comprises at least one term;

analyzing at least one of a plurality of information sources based on the at least one term and the topical domain; and

determining, based on the analyzing, that the object is one of ambiguous and unambiguous.

2. The method of claim 1, wherein analyzing the at least one of a plurality of information sources comprises:

analyzing, based on the at least one term and the topical domain, the plurality of information sources in a pipelined sequence.

3. The method of claim 2, wherein analyzing the plurality of information sources in a pipelined sequence comprises:

analyzing, based on the at least one term and the topical domain, a first of the plurality of information sources;

determining, based on analyzing the first of the plurality of information sources, if the object is ambiguous;

in response to determining that the object is ambiguous, storing an indication that the term is ambiguous;

in response to determining that the term is unambiguous, analyzing at least a second of the plurality of information sources based on the at least one term and the topical domain;

determining, based on analyzing the second of the plurality of information sources, if the term is ambiguous;

in response to determining that the object is ambiguous, storing an indication that the object is ambiguous; and

in response to determining that the object is unambiguous, storing an indication that the object is unambiguous.

4. The method of claim 2, wherein analyzing the plurality of information sources in a pipelined sequence comprises:

analyzing, based on the at least one term and the topical domain, a set of n-grams generated from at least one text corpus;

generating, based on the analyzing, a count of a number of times the at least one term appears in the text corpus;

determining if the count is greater than a threshold;

in response to determining that the count is greater than the threshold, storing an indication that the object is ambiguous;

in response to determining that the count is below the threshold, determining if at least one subsequent information source exists in the pipelined sequence;

in response to determining that at least one subsequent information source exists in the pipelined sequence, passing processing of the object to the at least one subsequent information source; and

in response to determining that at least one subsequent information source fails to exist in the pipelined sequence, storing an indication that the object is unambiguous.

5. The method of claim 2, wherein analyzing the plurality of information sources in a pipelined sequence comprises:

analyzing, based on the at least one term and the topical domain, a set of ontologies;

determining, based on the analyzing the set of ontologies, if the object comprises multiple senses within at least one of the set of ontologies;

in response to determining that the object comprises multiple senses within the at least one of the set of ontologies, storing an indication that the object is ambiguous;

in response to determining that the object fails to comprise multiple senses within the at least one of the set of ontologies, determining if at least one subsequent information source exists in the pipelined sequence;

in response to determining that at least one subsequent information source exists in the pipelined sequence, passing processing of the object to the at least one subsequent information source; and

in response to determining that at least one subsequent information source fails to exist in the pipelined sequence, storing an indication that the object is unambiguous.

6. The method of claim of claim 2, wherein analyzing the plurality of information sources in a pipelined sequence comprises:

analyzing, based on the at least one term and the topical domain, a set of document clusters, wherein each document comprises a set of words associated with the at least one term, and wherein each of the set of words is associated with a weight;

determining, based on analyzing the set of document clusters, if at least one of the domain and one or more synonyms of the domain appear in N highest weighted words in at least one of the set of document clusters;

in response to determining that at least one of the domain and one or more synonyms of the domain appears in the N highest weighted words in at least one of the set of document clusters, storing an indication that the object is ambiguous;

in response to determining that at least one of the domain and one or more synonyms of the domain fails to appear in the N highest weighted words in at least one of the set of document clusters, determining if at least one subsequent information source exists in the pipelined sequence;

in response to determining that at least one subsequent information source exists in the pipelined sequence, passing processing of the object to the at least one subsequent information source; and

in response to determining that at least one subsequent information source fails to exist in the pipelined sequence, storing an indication that the object is unambiguous.

7. The method of claim 1, wherein the plurality information sources comprise at least one of:

a set of text corpora;

a set of ontologies; and

a set of clustered documents.

8. An information processing system comprising:

a memory;

a processor communicatively coupled to the memory; and

an ambiguity detector communicatively coupled to the memory and the processor, wherein the ambiguity detector is configured to perform a method comprising;

obtaining an object and a topical domain associated with the object, wherein the object comprises at least one term;

analyzing at least one of a plurality of information sources based on the at least one term and the topical domain; and

determining, based on the analyzing, that the object is one of ambiguous and unambiguous.

9. The information processing system of claim 8, wherein analyzing the at least one of a plurality of information sources comprises:

analyzing, based on the at least one term and the topical domain, the plurality of information sources in a pipelined sequence.

10. The information processing system of claim 9, wherein analyzing the plurality of information sources in a pipelined sequence comprises:

analyzing, based on the at least one term and the topical domain, a first of the plurality of information sources;

determining, based on analyzing the first of the plurality of information sources, if the object is ambiguous;

in response to determining that the object is ambiguous, storing an indication that the term is ambiguous;

in response to determining that the term is unambiguous, analyzing at least a second of the plurality of information sources based on the at least one term and the topical domain;

determining, based on analyzing the second of the plurality of information sources, if the term is ambiguous;

in response to determining that the object is ambiguous, storing an indication that the object is ambiguous; and

in response to determining that the object is unambiguous, storing an indication that the object is unambiguous.

11. The information processing system of claim 9, wherein analyzing the plurality of information sources in a pipelined sequence comprises:

analyzing, based on the at least one term and the topical domain, a set of n-grams generated from at least one text corpus;

generating, based on the analyzing, a count of a number of times the at least one term appears in the text corpus;

determining if the count is greater than a threshold;

in response to determining that the count is greater than the threshold, storing an indication that the object is ambiguous;

in response to determining that the count is below the threshold, determining if at least one subsequent information source exists in the pipelined sequence;

in response to determining that at least one subsequent information source exists in the pipelined sequence, passing processing of the object to the at least one subsequent information source; and

in response to determining that at least one subsequent information source fails to exist in the pipelined sequence, storing an indication that the object is unambiguous.

12. The information processing system of claim 9, wherein analyzing the plurality of information sources in a pipelined sequence comprises:

analyzing, based on the at least one term and the topical domain, a set of ontologies;

determining, based on the analyzing the set of ontologies, if the object comprises multiple senses within at least one of the set of ontologies;

in response to determining that the object comprises multiple senses within the at least one of the set of ontologies, storing an indication that the object is ambiguous;

in response to determining that the object fails to comprise multiple senses within the at least one of the set of ontologies, determining if at least one subsequent information source exists in the pipelined sequence;

in response to determining that at least one subsequent information source exists in the pipelined sequence, passing processing of the object to the at least one subsequent information source; and

in response to determining that at least one subsequent information source fails to exist in the pipelined sequence, storing an indication that the object is unambiguous.

13. The information processing system of claim 9, wherein analyzing the plurality of information sources in a pipelined sequence comprises:

analyzing, based on the at least one term and the topical domain, a set of document clusters, wherein each document comprises a set of words associated with the at least one term, and wherein each of the set of words is associated with a weight;

determining, based on analyzing the set of document clusters, if at least one of the domain and one or more synonyms of the domain appear in N highest weighted words in at least one of the set of document clusters;

in response to determining that at least one of the domain and one or more synonyms of the domain appears in the N highest weighted words in at least one of the set of document clusters, storing an indication that the object is ambiguous;

in response to determining that at least one of the domain and one or more synonyms of the domain fails to appear in the N highest weighted words in at least one of the set of document clusters, determining if at least one subsequent information source exists in the pipelined sequence;

in response to determining that at least one subsequent information source exists in the pipelined sequence, passing processing of the object to the at least one subsequent information source; and

in response to determining that at least one subsequent information source fails to exist in the pipelined sequence, storing an indication that the object is unambiguous.

14. A computer program product comprising:

a non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code configured to perform a method comprising;

obtaining an object and a topical domain associated with the object, wherein the object comprises at least one term;

analyzing at least one of a plurality of information sources based on the at least one term and the topical domain; and

determining, based on the analyzing, that the object is one of ambiguous and unambiguous.

15. The computer program product of claim 14, wherein analyzing the at least one of a plurality of information sources comprises:

analyzing, based on the at least one term and the topical domain, the plurality of information sources in a pipelined sequence.

16. The computer program product of claim 15, wherein analyzing the plurality of information sources in a pipelined sequence comprises:

analyzing, based on the at least one term and the topical domain, a first of the plurality of information sources;

determining, based on analyzing the first of the plurality of information sources, if the object is ambiguous;

in response to determining that the object is ambiguous, storing an indication that the term is ambiguous;

in response to determining that the term is unambiguous, analyzing at least a second of the plurality of information sources based on the at least one term and the topical domain;

determining, based on analyzing the second of the plurality of information sources, if the term is ambiguous;

in response to determining that the object is ambiguous, storing an indication that the object is ambiguous; and

in response to determining that the object is unambiguous, storing an indication that the object is unambiguous.

17. The computer program product of claim 15, wherein analyzing the plurality of information sources in a pipelined sequence comprises:

analyzing, based on the at least one term and the topical domain, a set of n-grams generated from at least one text corpus;

generating, based on the analyzing, a count of a number of times the at least one term appears in the text corpus;

determining if the count is greater than a threshold;

in response to determining that the count is greater than the threshold, storing an indication that the object is ambiguous;

in response to determining that the count is below the threshold, determining if at least one subsequent information source exists in the pipelined sequence;

in response to determining that at least one subsequent information source exists in the pipelined sequence, passing processing of the object to the at least one subsequent information source; and

in response to determining that at least one subsequent information source fails to exist in the pipelined sequence, storing an indication that the object is unambiguous.

18. The computer program product of claim 15, wherein analyzing the plurality of information sources in a pipelined sequence comprises:

analyzing, based on the at least one term and the topical domain, a set of ontologies;

determining, based on the analyzing the set of ontologies, if the object comprises multiple senses within at least one of the set of ontologies;

in response to determining that the object comprises multiple senses within the at least one of the set of ontologies, storing an indication that the object is ambiguous;

in response to determining that the object fails to comprise multiple senses within the at least one of the set of ontologies, determining if at least one subsequent information source exists in the pipelined sequence;

in response to determining that at least one subsequent information source exists in the pipelined sequence, passing processing of the object to the at least one subsequent information source; and

in response to determining that at least one subsequent information source fails to exist in the pipelined sequence, storing an indication that the object is unambiguous.

19. The computer program product of claim 15, wherein analyzing the plurality of information sources in a pipelined sequence comprises:

analyzing, based on the at least one term and the topical domain, a set of document clusters, wherein each document comprises a set of words associated with the at least one term, and wherein each of the set of words is associated with a weight;

determining, based on analyzing the set of document clusters, if at least one of the domain and one or more synonyms of the domain appear in N highest weighted words in at least one of the set of document clusters;

in response to determining that at least one of the domain and one or more synonyms of the domain appears in the N highest weighted words in at least one of the set of document clusters, storing an indication that the object is ambiguous;

in response to determining that at least one of the domain and one or more synonyms of the domain fails to appear in the N highest weighted words in at least one of the set of document clusters, determining if at least one subsequent information source exists in the pipelined sequence;

in response to determining that at least one subsequent information source exists in the pipelined sequence, passing processing of the object to the at least one subsequent information source; and

in response to determining that at least one subsequent information source fails to exist in the pipelined sequence, storing an indication that the object is unambiguous.

20. The computer program product of claim 14, wherein the plurality information sources comprise at least one of:

a set of text corpora;

a set of ontologies; and

a set of clustered documents.