Abstract
This paper focuses on the task of knowledge-based question answering (KBQA). KBQA aims to match the questions with the structured semantics in knowledge base. In this paper, we propose a two-stage method. Firstly, we propose a topic entity extraction model (TEEM) to extract topic entities in questions, which does not rely on hand-crafted features or linguistic tools. We extract topic entities in questions with the TEEM and then search the knowledge triples which are related to the topic entities from the knowledge base as the candidate knowledge triples. Then, we apply Deep Structured Semantic Models based on convolutional neural network and bidirectional long short-term memory to match questions and predicates in the candidate knowledge triples. To obtain better training dataset, we use an iterative approach to retrieve the knowledge triples from the knowledge base. The evaluation result shows that our system achieves an \(\text {Average} F_1\) measure of 79.57% on test dataset.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 61303180 and No. 61573163), the Fundamental Research Funds for the Central Universities (No. CCNU15ZD003 and No. CCNU16A02024), and also supported by Wuhan Youth Science and technology plan. We thank the anonymous reviewers for their insightful comments.
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Xie, Z., Zeng, Z., Zhou, G., He, T. (2016). Knowledge Base Question Answering Based on Deep Learning Models. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_25
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