Title: Dynamic extended tree conditioned LSTM-based biomedical event extraction

Authors: Lishuang Li; Jieqiong Zheng; Jia Wan

Addresses: School of Computer Science and Technology, Dalian University of Technology, No. 2, Linggong Road, Hi-Tech Zone, Dalian 116024, China ' School of Computer Science and Technology, Dalian University of Technology, No. 2, Linggong Road, Hi-Tech Zone, Dalian 116024, China ' School of Computer Science and Technology, Dalian University of Technology, No. 2, Linggong Road, Hi-Tech Zone, Dalian 116024, China

Abstract: Extracting knowledge from unstructured text has become essential to the text mining and knowledge discovery tasks in biomedical field. In this paper, we propose a novel Long Short Term Memory (LSTM) networks framework DET-BLSTM to extract biomedical events among biotope and bacteria from biomedical literature. In our framework, a dynamic extended tree is introduced as the input instead of the original sentences, which utilises the syntactic information. Furthermore, the POS and distance embeddings are added to enrich input information. In final, considering that shallow machine learning methods can effectively take advantage of the domain expert experience, the predictions of SVM are used for post-processing. Our DET-BLSTM model with post-processing achieves 58.09% F-score in the test set, which is better than all official submissions to BioNLP-ST 2016 and 2.29% higher than the best system.

Keywords: long short term memory; SVM; dynamic extended tree; biomedical event extraction; deep learning.

DOI: 10.1504/IJDMB.2017.085283

International Journal of Data Mining and Bioinformatics, 2017 Vol.17 No.3, pp.266 - 278

Available online: 17 Jul 2017 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article