Structured use of external knowledge for event-based open domain question answering
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Towards light semantic processing for Question Answering
HLT-NAACL-TEXTMEANING '03 Proceedings of the HLT-NAACL 2003 workshop on Text meaning - Volume 9
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In question answering, two main kinds of matching methods for finding answer sentences for a question are term-based approaches -- which are simple, efficient, effective, and yield high recall -- and event-based approaches that take syntactic and semantic information into account. The latter often sacrifice recall for increased precision, but actually capture the meaning of the events denoted by the textual units of a passage or sentence. We propose a robust, data-driven method that learns the mapping between questions and answers using logistic regression and show that combining term-based and event-based approaches significantly outperforms the individual methods.