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| Entity Relations Extraction in Chinese Domain Based on Distant Supervision with Multi-feature Fusion |
| WANG Bin1, GUO Jianyi1, 2, XIAN Yantuan1, 2, WANG Hongbin1, 2, YU Zhengtao1, 2 |
1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500;
2.Key Laboratory of Intelligent Information Processing, Kunming University of Science and Technology, Kunming 650500 |
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Abstract Aiming at the extraction of Chinese domain entity relationship from unlabeled text, a hybrid method of domain entity attribute extraction based on distant supervision is proposed. The structured relational three tuples in the knowledge base are applied to obtain the training corpus automatically from the natural language text. Due to the large amount of noise in the annotation data of distant supervision method, the latent Dirichlet allocation(LDA) topic model for topic keyword extraction is adopted, and then the similarity calculation with relationship type and keyword pattern matching for denoising are performed. Finally, the part-of-speech feature, the dependency feature and the phrase syntax tree feature are extracted, and the relationship extraction model is trained. Experiments show that the method fusing three features produces higher F value and better extraction performance.
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Received: 15 October 2018
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| Fund:Supported by National Natural Science Foundation of China(No.61562052,61363044,61462054) |
| About author:: (WANG Bin, master student. His research interests include natural language proce-ssing.) (GUO Jianyi(Corresponding author), master, professor. Her research interests include pattern recognition, natural language processing, information extraction and knowledge acquisition.)(XIAN Yantuan, Ph.D. candidate, lectu-rer. His research interests include machine translation, information retrieval and information extraction.) (WANG Hongbin, Ph.D. candidate. His research interests include intelligent information system, natural language processing and information retrieval.) (YU Zhengtao, Ph.D., professor. His research interests include machine translation, natural language processing and information retrieval.) |
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2019, Vol. 32 
