Analyzing Linguistic Features for Answer Re-Ranking of Why-Questions

Abstract

Why-type non-factoid questions are ambiguous and involve variations in their answers. A challenge in returning one appropriate answer to users requires the process of appropriate answer extraction, re-ranking, and validation. There are cases where the need is to understand the meaning and context of a document rather than finding exact words involved in question. The paper addresses this problem by exploring lexico-syntactic, semantic, and contextual query-dependent features, some of which are based on deep learning frameworks to depict the probability of answer candidate being relevant for the question. The features are weighted by the score returned by ensemble ExtraTreesClassifier according to features importance. An answer re-ranker model is implemented that finds the highest ranked answer comprising largest value of feature similarity between question-and-answer candidate and thus achieving 0.64 mean reciprocal rank (MRR). Further, the answer is validated by matching the answer type of answer candidate and returning the highest-ranked answer candidate with matched answer type to a user.