2010 AComparativeStudyofWordCoOccurr

• (Momtazi et al., 2010) ⇒ Saeedeh Momtazi, Sanjeev Khudanpur, and Dietrich Klakow. (2010). “A Comparative Study of Word Co-occurrence for Term Clustering in Language Model-based Sentence Retrieval.” In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. ISBN:1-932432-65-5

Subject Headings: Lexical Co-Occurrence, Sentence Retrieval, Corpus Driven Clustering of Terms.

Cited By

• Google Scholar ~ 23 Citations.
• ACM DL: ~ 2 Citations.

Quotes

Abstract

Sentence retrieval is a very important part of question answering systems. Term clustering, in turn, is an effective approach for improving sentence retrieval performance: the more similar the terms in each cluster, the better the performance of the retrieval system. A key step in obtaining appropriate word clusters is accurate estimation of pairwise word similarities, based on their tendency to co-occur in similar contexts. In this paper, we compare four different methods for estimating word co-occurrence frequencies from two different corpora. The results show that different, commonly-used contexts for defining word co-occurrence differ significantly in retrieval performance. Using an appropriate co-occurrence criterion and corpus is shown to improve the mean average precision of sentence retrieval form 36.8% to 42.1%.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 AComparativeStudyofWordCoOccurr	Sanjeev Khudanpur Saeedeh Momtazi Dietrich Klakow			A Comparative Study of Word Co-occurrence for Term Clustering in Language Model-based Sentence Retrieval						2010