Publication Details
Title: A Model for Combining Semantic and Phonetic Term Similarity for Spoken Document and Spoken Query Retrieval
Author: F. Crestani
Group: ICSI Technical Reports
Date: December 1999
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1999/tr-99-020.pdf
Overview:
In classical Information Retrieval systems a relevant document will not be retrieved in response to a query if the document and query representations do not share at least one term. This problem is known as "term mismatch''. A similar problem can be found in spoken document retrieval and spoken query processing, where terms misrecognized by the speech recognition process can hinder the retrieval of potentially relevant documents. We will call this problem "term misrecognition'', by analogy to the term mismatch problem. Here we present two classes of retrieval models that attempt to tackle both the term mismatch and the term misrecognition problems at retrieval time using term similarity information. The models assume the availability of complete or partial knowledge of semantic and phonetic term-term similarity in the index term space.
Bibliographic Information:
ICSI Technical Report TR-99-020
Bibliographic Reference:
F. Crestani. A Model for Combining Semantic and Phonetic Term Similarity for Spoken Document and Spoken Query Retrieval. ICSI Technical Report TR-99-020, December 1999
Author: F. Crestani
Group: ICSI Technical Reports
Date: December 1999
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1999/tr-99-020.pdf
Overview:
In classical Information Retrieval systems a relevant document will not be retrieved in response to a query if the document and query representations do not share at least one term. This problem is known as "term mismatch''. A similar problem can be found in spoken document retrieval and spoken query processing, where terms misrecognized by the speech recognition process can hinder the retrieval of potentially relevant documents. We will call this problem "term misrecognition'', by analogy to the term mismatch problem. Here we present two classes of retrieval models that attempt to tackle both the term mismatch and the term misrecognition problems at retrieval time using term similarity information. The models assume the availability of complete or partial knowledge of semantic and phonetic term-term similarity in the index term space.
Bibliographic Information:
ICSI Technical Report TR-99-020
Bibliographic Reference:
F. Crestani. A Model for Combining Semantic and Phonetic Term Similarity for Spoken Document and Spoken Query Retrieval. ICSI Technical Report TR-99-020, December 1999