A retrieval-based matching approach to open domain knowledge-based question answering

Abstract

In this paper, we propose a retrieval and knowledge-based question answering system for the competition task in NLPCC 2017. Regarding the question side, our system uses a ranking model to score candidate entities to detect a topic entity from questions. Then similarities between the question and candidate relation chains are computed, based on which candidate answer entities are ranked. By returning the highest scored answer entity, our system finally achieves the F1-score of 41.96% on test set of NLPCC 2017. Our current system focuses on solving single-relation questions, but it can be extended to answering multiple-relation questions.