LANGUAGE MODEL ADAPTATION BASED ON FILTERED DATA

• US 20150278192A1
• Filed: 03/25/2014
• Published: 10/01/2015
• Est. Priority Date: 03/25/2014
• Status: Active Grant

• Current Assignee: Nice Ltd
• Original Assignee: Nice Systems Limited (Nice Ltd)
• Inventors: Nissan, Maor, Bretter, Ronny, Artzi, Shimrit
• Granted Patent: US 9,564,122 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G10L 15/00 Speech recognition G10L17/00 takes precedence
• CPC Class Codes: G10L 15/065 Adaptation
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models

Abstract

A method for adapting a language model for a context of a domain, comprising obtaining textual contents from a large source by a request directed to the context of the domain, discarding at least a part of the textual contents that contain textual terms determined as irrelevant to the context of the domain, thereby retaining, as retained data, at least a part of the textual contents that contain textual terms determined as relevant to the context of the domain, and adapting the language model by incorporating therein at least a part of the textual terms of the retained data, wherein the method is performed on an at least one computerized apparatus configured to perform the method and equipped for communication with the large source, and an apparatus for performing the same.

Claims

1. A method for adapting a language model for a context of a domain, comprising:

obtaining textual contents from a large source by a request directed to the context of the domain;

discarding at least a part of the textual contents that contain textual terms determined as irrelevant to the context of the domain, thereby retaining, as retained data, at least a part of the textual contents that contain textual terms determined as relevant to the context of the domain; and

adapting the language model by incorporating therein at least a part of the textual terms of the retained data,

wherein the method is performed on an at least one computerized apparatus configured to perform the method and equipped for communication with the large source.

2. The method according to claim 1, wherein at least a part of the retained data having at least one textual term which do not match any element in a provided set of at least one textual term is discarded prior to adapting the language model.

3. The method according to claim 1, wherein the at least a part of the textual contents that contains textual terms determined as irrelevant to the context of the domain comprises a plurality of parts of the textual contents that contains textual terms determined as irrelevant to the context of the domain.

4. The method according to claim 1, wherein the at least a part of the textual content that contains textual terms determined as relevant to the context of the domain comprises a plurality of parts of the textual contents that contain textual terms determined as relevant to the context of the domain.

5. The method according to claim 1, wherein the textual contents are further obtained via a provided at least one link to the large source.

6. The method according to claim 1, wherein the large source is at least a part of the Web.

7. A method for adapting a baseline language model for a context of a domain by data of the Web, comprising:

obtaining, from the domain, data representative of the context of the domain;

based on the data representative of the context of the domain, forming a query that is provided to an at least one search engine of the Web, thereby acquiring an at least one result comprising textual contents;

discarding at least a part of the at least one result in which the textual contents includes at least one textual term that does not pertain to the data representative of the context of the domain;

adapting the baseline language model to an adapted language model by incorporating therein textual terms of the at least one result that pertain to the data representative of the context of the domain,

wherein the method is performed on an at least one computerized apparatus configured to perform the method and equipped for communication with at least one computerized server linkable to the Web.

8. The method according to claim 7, wherein the discarding of the at least a part of the at least one result comprises discarding all parts of the at least one result in which the textual contents comprises at least one textual term that does not pertain to the data representative of the context of the domain.

9. The method according to claim 7, wherein the discarding of the at least a part of the at least one result comprises discarding the at least one results in which the textual content comprises at least one textual term that does not pertain to the data representative of the context of the domain.

10. The method according to claim 7, further discarding at least a part of the at least one result having textual terms which do not match any element in a provided set of at least one textual term.

11. The method according to claim 7, wherein the acquiring at least one result further comprises acquiring further textual contents via a provided at least one link to a Web site.

12. The method according to claim 7, wherein the at least one result comprises a plurality of results.

13. The method according to claim 7, wherein the textual terms that pertain to the data representative of the context of the domain are determined based on a semantic relationship between the textual terms and the data representative of the context of the domain.

14. The method according to claim 7, further comprising evaluating the adapted language model by comparing the performance of the adapted language model with the performance of the baseline language model in recognizing textual terms in a provided speech data comprising coded textual terms related to the domain.

15. The method according to claim 14, wherein the provided data is an audio data comprising spoken words related to the domain.