GENERATION OF DOMAIN MODELS FROM NOISY TRANSCRIPTIONS

• US 20080177538A1
• Filed: 03/26/2008
• Published: 07/24/2008
• Est. Priority Date: 10/13/2006
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: International Business Machines Corporation
• Inventors: Roy, Shourya, Subramaniam, Laxminarayan Venkata
• Granted Patent: US 8,626,509 B2
• Time in Patent Office: Days
• US Class Current: 704/235
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence

Abstract

A method of building a domain specific model from transcriptions is disclosed. The method starts by applying text clustering to the transcriptions to form text clusters. The text clustering is applied at a plurality of different granularities, and groups topically similar phrases in the transcriptions. The relationship between text clusters resulting from the text clustering at different granularities is then identified to form a taxonomy. The taxonomy is augmented with topic specific information.

Claims

1. A method of building a domain specific model from transcriptions, said method comprising the steps of:

applying text clustering to said transcriptions to form text clusters, where said text clustering is applied at a plurality of different granularities and groups topically similar phrases in said transcriptions;

identifying the relationship between text clusters resulting from said text clustering at different granularities to form a taxonomy; and

augmenting said taxonomy with topic specific information.

2. The method according to claim 1, all the limitations of which are incorporated herein by reference, wherein said taxonomy is augmented with one or more of:

typical issues raised and solutions to those issues;

typical questions and answers; and

statistics relating to conversations associated with said transcriptions.

3. The method according to claim 1, all the limitations of which are incorporated herein by reference, comprising the initial step of:

extracting from said transcriptions n-grams based upon the respective frequencies of occurrences of the n-grams.

4. The method according to claim 1, all the limitations of which are incorporated herein by reference, wherein said identifying step identifies relationships between text clusters resulting from said text clustering at granularities of adjacent levels.

5. The method according to claim 1, all the limitations of which are incorporated herein by reference, comprising the initial steps of:

removing stopwords from said transcriptions; and

removing pause filling words from said transcriptions.

6. The method according to claim 5, all the limitations of which are incorporated herein by reference, wherein said stopwords include generic stopwords and domain specific stopwords.

7. A method comprising the steps of:

receiving a domain specific model;

receiving a transcription of a part of a conversation; and

mapping said transcription to a node in said domain specific model.

8. A method comprising the steps of:

receiving a domain specific model;

receiving a transcription of a conversation;

calculating statistics of said transaction; and

comparing at least said statistics of said transcription with statistics from said domain specific model.

9. An apparatus for building a domain specific model from transcriptions, said apparatus comprising a processor configured to perform the steps comprising of:

applying text clustering to said transcriptions to form text clusters, where said text clustering is applied at a plurality of different granularities and groups topically similar phrases in said transcriptions;

identifying the relationship between text clusters resulting from said text clustering at different granularities to form a taxonomy; and

augmenting said taxonomy with topic specific information.

10. The apparatus according to claim 10, all the limitations of which are incorporated herein by reference, wherein said taxonomy is augmented with one or more of:

typical issues raised and solutions to those issues;

typical questions and answers; and

statistics relating to conversations associated with said transcriptions.

11. The apparatus according to claim 10, all the limitations of which are incorporated herein by reference, wherein said processor is configured to perform the further step of:

extracting from said transcriptions n-grams based upon the respective frequencies of occurrences of the n-grams.

12. The apparatus according to claim 10, all the limitations of which are incorporated herein by reference, wherein said identifying step identifies relationships between text clusters resulting from said text clustering at granularities of adjacent levels.

13. An apparatus configured to perform the steps comprising of:

receiving a domain specific model;

receiving a transcription of a part of a conversation; and

mapping said transcription to a node in said domain specific model.

14. An apparatus configured to perform the steps comprising of:

receiving a domain specific model;

receiving a transcription of a conversation;

calculating statistics of said transaction; and

comparing at least said statistics of said transcription with statistics from said domain specific model.