Open AccessCombined Networks with Multi-level Attention for Distantly-Supervised Relation Extraction
Author(s) -
Hongyang Yuan
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1550/3/032065
Subject(s) - relationship extraction , computer science , convolutional neural network , artificial intelligence , sentence , piecewise , relation (database) , representation (politics) , information extraction , pattern recognition (psychology) , sequence (biology) , artificial neural network , extraction (chemistry) , machine learning , data mining , mathematics , mathematical analysis , chemistry , genetics , chromatography , politics , biology , political science , law
Distantly-supervised relation extraction is expected to extract relational facts from very large corpora. However, it is inevitably accompanied by the problem of mislabeling, which affects the performance of relation extraction. To obtain richer information from sentences and make full use of the information of entity pairs, we propose the combined networks containing piecewise convolutional neural networks (PCNN) and bidirectional gated recurrent units (BiGRU) with multi-level attention. In this model, PCNN and BiGRU are utilized to obtain local features and sequence information. Then the multi-level attention is proposed to extract the most correlated information, which can affect the assignment of sentence attention weight. And it reduces the influence of wrong labeled instances indirectly. The experimental results show that our method can enhance the representation power of the network and prove the effectiveness of our model compared with several baseline methods.