Automatic Feature Engineering for Answer Selection and Extraction

This paper proposes a framework for automat-ically engineering features for two important tasks of question answering: answer sentence selection and answer extraction. We represent question and answer sentence pairs with lin-guistic structures enriched by semantic infor-mation, where the latter is produced by auto-matic classifiers, e.g., question classifier and Named Entity Recognizer. Tree kernels ap-plied to such structures enable a simple way to generate highly discriminative structural fea-tures that combine syntactic and semantic in-formation encoded in the input trees. We con-duct experiments on a public benchmark from TREC to compare with previous systems for answer sentence selection and answer extrac-tion. The results show that our models greatly improve on the state of the art, e.g., up to 22% on F1 (relative improvement) for answer ex-traction, while using no additional resources and no manual feature engineering.