Abstract
Aiming at the task of open domain question answering based on knowledge base in NLP&CC 2016, we propose a SPE (subject predicate extraction) algorithm which can automatically extract a subject-predicate pair from a simple question and translate it to a KB query. A novel method based on word vector similarity and predicate attention is used to score the candidate predicate after a simple topic entity linking method. Our approach achieved the F1-score of 82.47% on test data which obtained the first place in the contest of NLP&CC 2016 Shared Task 2 (KBQA sub-task). Furthermore, there are also a series of experiments and comprehensive error analysis which can show the properties and defects of the new data set.
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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, 2014AA015102), Natural Science Foundation of China (Grant No. 61202233, 61272344, 61370055) and the joint project with IBM Research. Any correspondence please refer to Yansong Feng.
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Lai, Y., Lin, Y., Chen, J., Feng, Y., Zhao, D. (2016). Open Domain Question Answering System Based on Knowledge Base. 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_65
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DOI: https://doi.org/10.1007/978-3-319-50496-4_65
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