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ERCIMDL
2010
Springer

Effective Term Weighting for Sentence Retrieval

9 years 10 months ago
Effective Term Weighting for Sentence Retrieval
A well-known challenge of information retrieval is how to infer a user's underlying information need when the input query consists of only a few keywords. Question Answering (QA) systems face an equally important but opposite challenge: given a verbose question, how can the system infer the relative importance of terms in order to differentiate the core information need from supporting context? We investigate three simple term-weighting schemes for such estimation within the language modeling retrieval paradigm [6]. While the three schemes described are ad hoc, they address a principled estimation problem underlying the standard word unigram model. We also show these schemes enable better estimation of a state-of-the-art class model based on term clustering [5]. Using a TREC QA dataset, we evaluate the three weighting schemes for both word and class models on the QA subtask of sentence retrieval. Our inverse sentence frequency weighting scheme achieves over 5% absolute improvement...
Saeedeh Momtazi, Matthew Lease, Dietrich Klakow
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ERCIMDL
Authors Saeedeh Momtazi, Matthew Lease, Dietrich Klakow
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