Answer Ranking based on Named Entity Types for Question Answering

Abstract

Question answering (QA) using triples has been widely studied. One important aspect is answer ranking, that is, which answer candidates should be used to find correct answers. We are proposing a new method using type-matching information for ranking QA triples. We recommend using a new ranking method that includes type-matching scores from semantic answer types, and named entity types of answer candidates. Our experiments provided new ranking results with ranking average values that were lower by 24.80. We conclude that our proposed method has great potential for answer ranking.