Open AccessAnalyzing Linguistic Features for Answer Re-Ranking of Why-Questions
Publication year - 2022
Publication title -
journal of cases on information technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.228
H-Index - 14
eISSN - 1548-7725
pISSN - 1548-7717
DOI - 10.4018/jcit.20220801oa02
Subject(s) - ranking (information retrieval) , information retrieval , computer science , mean reciprocal rank , natural language processing , context (archaeology) , rank (graph theory) , meaning (existential) , question answering , artificial intelligence , feature (linguistics) , matching (statistics) , reciprocal , similarity (geometry) , learning to rank , questions and answers , linguistics , mathematics , psychology , statistics , paleontology , philosophy , combinatorics , image (mathematics) , psychotherapist , biology
Why-type non-factoid questions are ambiguous and involve variations in their answers. A challenge in returning one appropriate answer to user 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, answer is validated by matching the answer type of answer candidate and returns the highest ranked answer candidate with matched answer type to a user.