Combination of similarity measures for effective spoken document retrieval

被引:8
|
作者
Crestani, F [1 ]
机构
[1] Univ Strathclyde, Dept Comp & Informat Sci, Glasgow G1 1XH, Lanark, Scotland
关键词
D O I
10.1177/016555103763031572
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:87 / 96
页数:10
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