Generation of domain models from noisy transcriptions

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.

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  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  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  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  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  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 9  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 9  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 9  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.