Bacteria Biotope Relation Extraction Based on a Fusion Neural Network

被引：0
作者：
Li M. [1 ]
Wang J. [1 ]
Wang Y. [1 ]
Lin H. [1 ]
Yang Z. [1 ]
机构：
[1] School of Computer Science and Technology, Dalian University of Technology, Dalian
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 02期
基金：
中国国家自然科学基金; 国家重点研发计划;
关键词：
Convolutional Neural Network(CNN); Long Short-Term Memory(LSTM); Relation Extraction;
D O I：
10.16451/j.cnki.issn1003-6059.201902010
中图分类号：
学科分类号：
摘要：
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. © 2019, Science Press. All right reserved.
引用
收藏
页码：177 / 183
页数：6
相关论文
共 14 条
  • [1] Nedellec C., Bossy R., Kim J.D., Et al., Overview of BioNLP Shared Task 2013, Proc of the BioNLP Shared Task 2013 Workshop, pp. 1-7, (2013)
  • [2] Wang J., Li H.L., Lin H.F., Et al., Bacteria Biotope Extraction on the Basis of Neural Network, Journal of South China University of Technology(Natural Science Edition), 45, 3, pp. 76-81, (2017)
  • [3] Bjorne J., Heimonen J., Ginter F., Et al., Extracting Complex Biological Events with Rich Graph-Based Feature Sets, Proc of the Workshop on BioNLP: Shared Task, pp. 10-18, (2009)
  • [4] Ratkovic Z., Golik W., Warnier P., Et al., BioNLP 2011 Task Bacteria Biotope: The Alvis System, Proc of the BioNLP Shared Task 2011 Workshop, pp. 102-111, (2011)
  • [5] Karadeniz I., Ozgur A., Bacteria Biotope Detection, Ontology-Based Normalization, and Relation Extraction Using Syntactic Rules, Proc of the BioNLP Shared Task 2013 Workshop, pp. 170-177, (2013)
  • [6] Li L.S., Liu S.S., Qin M.Y., Et al., Extracting Biomedical Event with Dual Decomposition Integrating Word Embeddings, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13, 4, pp. 669-677, (2016)
  • [7] Lever J., Jones S.J., VERSE: Event and Relation Extraction in the BioNLP 2016 Shared Task, Proc of the 4th BioNLP Shared Task Workshop, pp. 42-49, (2016)
  • [8] Lecun Y., Bengio Y., Hinton G., Deep Learning, Nature, 521, 7553, pp. 436-444, (2015)
  • [9] Collobert R., Weston J., Karlen M., Et al., Natural Language Processing(Almost) from Scratch, Journal of Machine Lear-ning Research, 12, pp. 2493-2537, (2011)
  • [10] Bossy R., Golik W., Ratkovic Z., Et al., BioNLP Shared Task 2013-An Overview of the Bacteria Biotope Task, Proc of the Bionlp Shared Task 2013 Workshop, pp. 161-169, (2013)
12