A Probabilistic Framework for Answer Selection in Question Answering

This paper describes a probabilistic answer selection framework for question answering. In contrast with previous work using individual resources such as ontologies and the Web to validate answer candidates, our work focuses on developing a unified framework that not only uses multiple resources for validating answer candidates, but also considers evidence of similarity among answer candidates in order to boost the ranking of the correct answer. This framework has been used to select answers from candidates generated by four different answer extraction methods. An extensive set of empirical results based on TREC factoid questions demonstrates the effectiveness of the unified framework.

• Type: Conference
• Year: 2007
• Where: NAACL
• Authors: Jeongwoo Ko, Luo Si, Eric Nyberg
• Keywords: Answer Candidates, Answer Selection Framework, Computational Linguistics, NAACL 2007, Unified Framework