Language-independent Probabilistic Answer Ranking for Question Answering

This paper presents a language-independent probabilistic answer ranking framework for question answering. The framework estimates the probability of an individual answer candidate given the degree of answer relevance and the amount of supporting evidence provided in the set of answer candidates for the question. Our approach was evaluated by comparing the candidate answer sets generated by Chinese and Japanese answer extractors with the re-ranked answer sets produced by the answer ranking framework. Empirical results from testing on NTCIR factoid questions show a 40% performance improvement in Chinese answer selection and a 45% improvement in Japanese answer selection.

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