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.
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Acknowledgement
We would like to thank members in our NLP group and the anonymous reviewers for their helpful feedback. This work was supported by National High Technology R&D Program of China (Grant No. 2015AA015403), Natural Science Foundation of China (Grant No. 61672057, 61672058).
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Lai, Y., Jia, Y., Lin, Y., Feng, Y., Zhao, D. (2018). A Chinese Question Answering System for Single-Relation Factoid Questions. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_11
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