Sentence Retrieval is the task of retrieving a relevant sentence in
response to a query, a question, or a reference sentence. In this
work we begin by demonstrating that because sentences are much
smaller than documents, the performance of typical document
retrieval systems on the retrieval of sentences is significantly
worse. We propose several solutions to the problem of sentence
retrieval, based on statistical translation models, and investigate
these solutions the application areas of sentence retrieval for
question answering, novelty detection, and information provenance.
Statistical translation models are appropriate for tasks where the
sentence to be retrieved benefits from the addition of related
terms and synonyms. The context of a sentence affects its meaning,
and smoothing from the local context of the sentence improves
retrieval. A brief investigation of conditional models for sentence
retrieval suggests conditional models outperform language modeling
approaches, for some tasks. This book is addressed to students and
professionals working on language technology systems that are
dependent on sentence or passage retrieval.
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