Chinese entity attributes extraction based on bidirectional LSTM networks. (2019) - He, Zhonghe
For the low performance of slot filling method applied in Chinese entity - attribute extraction at present, this paper presents a distant supervision relation extraction method based on bidirectional long short-term memory neural network. First we get the Infobox of Baidu baike, using relation triples of Infobox to get the training corpus from the internet and then we train the classifier based on bidirectional LSTM Networks. Compared with classical methods, the method of this paper is fully automatic in the aspect of data annotation and feature extraction. Experiment results show that the proposed method is effective and it is suitable for information extraction in high dimensional space. Compared with the SVM algorithm, the accuracy rate is significantly improved.