Crestani, F. (2003) Combination of Similarity Measures for Effective Spoken Document Retrieval. Journal of Information Science, 29 (2). pp. 87-96. ISSN 0165-5515Full text not available in this repository. (Request a copy from the Strathclyde author)
Often users of information retrieval systems and document authors use different terms to refer to the same concept. For this simple reason, information retrieval is affected by the 'term mismatch' problem. The term mismatch problem does not only have the effect of hindering the retrieval of relevant documents, it also produces bad rankings of relevant documents. A similar problem can be found in spoken document retrieval, where terms misrecognized by the speech recognition process can hinder the retrieval of potentially relevant spoken documents. We will call this problem 'term misrecognition', by analogy to the term mismatch problem. This paper presents 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 use either complete or partial knowledge of semantic and phonetic term similarity, evaluated using statistical methods from the corpus.
|Keywords:||similarity measures, information retrieval, spoken document retrieval, Electronic computers. Computer science, Library and Information Sciences, Information Systems|
|Subjects:||Science > Mathematics > Electronic computers. Computer science|
|Department:||Faculty of Science > Computer and Information Sciences|
|Depositing user:||Strathprints Administrator|
|Date Deposited:||06 Feb 2007|
|Last modified:||22 Mar 2017 09:11|