Chinese Personal Relation Extraction Method Based on Convolutional Neural Network

doi:10.3969/j.issn.1006-2475.2018.09.004

Abstract

Abstract: Focused on the problem that the features need to be selected manually in personal relation extraction based on machine learning, a Chinese personal relation extraction method based on convolutional neural networks is proposed. The Word2vec model is trained by the Internet Chinese news corpus of Sogou Lab, and the expression of word vector based on distributed representation is obtained, and the transformation of the word vector for the Baidu encyclopedia data set is completed. A Chinese personal relation extraction system based on the classic CNN model is designed. The features are automatically extracted and the personal relation is classified by the CNN model. The accuracy rate reaches to 92.87%, and the average recall rate reaches to 86.92% in extraction of 5 kinds of personal relation. Experimental results show that this method does not need to construct complex features artificially, and it can get a better effect in personal relation extraction.

Key words: text mining, personal relation extraction, convolutional neural network, classification, word vector feature

CLC Number:

TP391

SI Wen-hao1, JIA Lei-ping2, QI Yin-cheng2. Chinese Personal Relation Extraction Method Based on Convolutional Neural Network[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2018.09.004.

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