Learning Unsupervised SVM Classifier for Answer Selection in Web Question Answering

Previous machine learning techniques for answer selection in question answering (QA) have required question-answer training pairs. It has been too expensive and labor-intensive, however, to collect these training pairs. This paper presents a novel unsupervised support vector machine (USVM) classifier for answer selection, which is independent of language and does not require 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. selecting the correct answer from the candidates by classifying the question. The comparative experiments demonstrate that the proposed approach significantly outperforms the retrieval-based model (Retrieval-M), the supervised SVM classifier (S-SVM), and the pattern-based model (Pattern-M) for answer selection. Moreover, the cross-model comparison showed that the performance ranking of these models was: U-SVM > PatternM > S-SVM > Retrieval-M.