On modeling of information retrieval concepts in vector spaces
ACM Transactions on Database Systems (TODS)
An outline of a general model for information retrieval systems
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Progress in the application of natural language processing to information retrieval tasks
The Computer Journal - Special issue on information retrieval
On modeling information retrieval with probabilistic inference
ACM Transactions on Information Systems (TOIS)
Using speech recognition
Solving the word mismatch problem through automatic text analysis
Solving the word mismatch problem through automatic text analysis
Vocal access to a newspaper archive: design issues and preliminary investigations
Proceedings of the fourth ACM conference on Digital libraries
Phonetic confusion matrix based spoken document retrieval
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval
Exploiting the Similarity of Non-Matching Terms at RetrievalTime
Information Retrieval
Effects of Word Recognition Errors in Spoken Query Processing
ADL '00 Proceedings of the IEEE Advances in Digital Libraries 2000
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Automatic Information Organization and Retrieval.
Automatic Information Organization and Retrieval.
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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. I 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 make effective use of complete or partial knowledge of semantic and phonetic term similarity evaluated using statistical methods for the corpus.