Paper: Language-independent Probabilistic Answer Ranking for Question Answering

ACL ID P07-1099
Title Language-independent Probabilistic Answer Ranking for Question Answering
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

This paper presents a language-independent probabilistic answer ranking framework for question answering. The framework esti- mates the probability of an individual an- swer candidate given the degree of answer relevance and the amount of supporting evi- dence provided in the set of answer candi- dates for the question. Our approach was evaluated by comparing the candidate an- swer sets generated by Chinese and Japanese answer extractors with the re-ranked answer sets produced by the answer ranking frame- work. Empirical results from testing on NT- CIR factoid questions show a 40% perfor- mance improvement in Chinese answer se- lection and a 45% improvement in Japanese answer selection.