Entity Relation Extraction Based on Convolutional Neural Network and Keywords Strategy
WANG Linyu1, WANG Li1, ZHENG Tingyi1,2
1.College of Computer Science and Technology, Taiyuan University of Technology, Jinzhong 030600 2. Department of Electrical and Power Engineering, Shanxi Institute of Energy, Jinzhong 030600
Abstract：The conventional relation extraction methods are time consuming, the error propagation in feature selection is likely to emerge, and the deep learning methods only depend on word embeddings to learn features. Aiming at these problems, a relation extraction method based on convolutional neural network and keywords strategy is proposed. Based on feature of the word embeddings, the keywords feature is acquired by the term proportion-inverse sentence proportion (TP-ISP) keywords extraction algorithm based on sentence. Thus, the category division is increased and the deficiency of the network to automatically learn features from sentence is remedied. In the network training process, the chunk-based max pooling strategy is adopted to reduce the information loss by the traditional max-over-time pooling strategy. The experiment demonstrates that the proposed method improves the results of entity relation extraction.
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2017, Vol. 30 
