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Computational Linguistics

Hwee Tou Ng, Editor
June 2010, Vol. 36, No. 2, Pages 229-245
(doi: 10.1162/coli.09-032-R1-08-034)
© 2010 Association for Computational Linguistics
What Is Not in the Bag of Words for Why-QA?
Article PDF (115.03 KB)

While developing an approach to why-QA, we extended a passage retrieval system that uses off-the-shelf retrieval technology with a re-ranking step incorporating structural information. We get significantly higher scores in terms of MRR@150 (from 0.25 to 0.34) and success@10. The 23% improvement that we reach in terms of MRR is comparable to the improvement reached on different QA tasks by other researchers in the field, although our re-ranking approach is based on relatively lightweight overlap measures incorporating syntactic constituents, cue words, and document structure.