Paper: Reranking Answers For Definitional QA Using Language Modeling

ACL ID P06-1136
Title Reranking Answers For Definitional QA Using Language Modeling
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

Statistical ranking methods based on cen- troid vector (profile) extracted from ex- ternal knowledge have become widely adopted in the top definitional QA sys- tems in TREC 2003 and 2004. In these approaches, terms in the centroid vector are treated as a bag of words based on the independent assumption. To relax this as- sumption, this paper proposes a novel language model-based answer reranking method to improve the existing bag-of- words model approach by considering the dependence of the words in the centroid vector. Experiments have been conducted to evaluate the different dependence models. The results on the TREC 2003 test set show that the reranking approach with biterm language model, significantly outperforms the one with the bag-of- words model and unigram language model by 14.9...