基于融合式神经网络的微生物生长环境关系抽取

doi:10.16451/j.cnki.issn1003-6059.201902010

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Abstract To build a complete bacteria biotope relation database, a relation extraction system based on a convolutional neural network(CNN)-long short-term memory(LSTM) model is proposed. Combining CNN and LSTM, the deep learning of hidden features are realized, and the distributed word vector feature and entity position feature are extracted as feature input of the model.Comparative experiments verify the advantages of CNN-LSTM model after the addition of features.The feature output of the CNN model is taken as the feature input of the LSTM model, and the best result is obtained on the BB-event corpus published by the Bio-NLP 2016 shared task.

Key words： Convolutional Neural Network(CNN) Long Short-Term Memory(LSTM) Relation Extraction

Received: 20 October 2018

ZTFLH:

TP 391

Fund:Supported by National Key R&D Program of China(No.2016YFB1001103), National Natural Science Foundation of China(No.61572098)

About author:: (LI Mengying, master student. Her research interests include natural language processing.)(WANG Jan(Corresponding author), Ph.D., professor. Her research interests include natural language processing.)(WANG Yan, Ph.D. candidate. His research interests include natural language processing.)(LIN Hongfei, Ph.D., professor. His research interests include natural language processing.) (YANG Zhihao, Ph.D., professor. His research interests include natural language processing.)

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	LI Mengying
	WANG Jian
	WANG Yan
	LIN Hongfei
	YANG Zhihao

Cite this article:

LI Mengying,WANG Jian,WANG Yan等. Bacteria Biotope Relation Extraction Based on a Fusion Neural Network[J]. , 2019, 32(2): 177-183.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201902010 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I2/177

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