Paper: Learning Unsupervised SVM Classifier for Answer Selection in Web Question Answering

ACL ID D07-1004
Title Learning Unsupervised SVM Classifier for Answer Selection in Web Question Answering
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

Previous machine learning techniques for answer selection in question answering (QA) have required question-answer train- ing pairs. It has been too expensive and labor-intensive, however, to collect these training pairs. This paper presents a novel unsupervised support vector machine (U- SVM) classifier for answer selection, which is independent of language and does not re- quire hand-tagged training pairs. The key ideas are the following: 1. unsupervised learning of training data for the classifier by clustering web search results; and 2. select- ing the correct answer from the candidates by classifying the question. The compara- tive experiments demonstrate that the pro- posed approach significantly outperforms the retrieval-based model (Retrieval-M), the supervised SVM classifier (S-SV...