A Chinese question answering system for single-relation factoid questions

Abstract

Aiming at the task of open domain question answering based on knowledge base in NLPCC 2017, we build a question answering system which can automatically find the promised entities and predicates for single-relation questions. After a features based entity linking component and a word vector based candidate predicates generation component, deep convolutional neural networks are used to rerank the entity-predicate pairs, and all intermediary scores are used to choose the final predicted answers. Our approach achieved the F1-score of 47.23% on test data which obtained the first place in the contest of NLPCC 2017 Shared Task 5 (KBQA sub-task). Furthermore, there are also a series of experiments which can help other developers understand the contribution of every part of our system.