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Reranking answers for definitional QA using language modeling

Yi Chen, Ming Zhou, Shilong Wang
2006 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06  
Statistical ranking methods based on centroid vector (profile) extracted from external knowledge have become widely adopted in the top definitional QA systems 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 assumption, this paper proposes a novel language model-based answer reranking method to improve the existing bag-ofwords model approach by considering the dependence of the words in the
more » ... entroid 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-ofwords model and unigram language model by 14.9% and 12.5% respectively in F-Measure(5).
doi:10.3115/1220175.1220311 dblp:conf/acl/ChenZW06 fatcat:7if62jfzkbbkjfjprnz5at2vxm