Distant Supervised Relation Extraction via DiSAN-2CNN on a Feature Level

Xueqiang Lv, Huixin Hou, Xindong You, Xiaopeng Zhang and Junmei Han
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Xueqiang Lv: Beijing Information Science & Technology University, China
Huixin Hou: Beijing Information Science & Technology University, China
Xindong You: Beijing Information Science & Technology University, China
Xiaopeng Zhang: Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, China
Junmei Han: Laboratory of Complex systems. Institute of Systems Engineering. AMS. PLA, China

International Journal on Semantic Web and Information Systems (IJSWIS), 2020, vol. 16, issue 2, 1-17

Abstract: At present, the mainstream distant supervised relation extraction methods existed problems: the coarse granularity for coding the context feature information; the difficulty in capturing the long-term dependency in the sentence, and the difficulty in coding prior knowledge of structures are major issues. To address these problems, we propose a distant supervised relation extraction model via DiSAN-2CNN on feature level, in which multi-dimension self-attention mechanism is utilized to encode the features of the words and DiSAN-2CNN is used to encode the sentence to obtain the long-term dependency, the prior knowledge of the structure, the time sequence, and the entity dependence in the sentence. Experiments conducted on the NYT-Freebase benchmark dataset demonstrate that the proposed DiSAN-2CNN on a feature level model achieves better performance than the current two state-of-art distant supervised relation extraction models PCNN+ATT and ResCNN-9, and it has d generalization ability with the least artificial feature engineering.

Date: 2020
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