Biomedical event extraction with a novel combination strategy based on hybrid deep neural networks

Background Biomedical event extraction is a fundamental and in-demand technology that has attracted substantial interest from many researchers. Previous works have heavily relied on manual designed features and external NLP packages in which the feature engineering is large and complex. Additionally, most of the existing works use the pipeline process that breaks down a task into simple sub-tasks but ignores the interaction between them. To overcome these limitations, we propose a novel event combination strategy based on hybrid deep neural networks to settle the task in a joint end-to-end manner. Results We adapted our method to several annotated corpora of biomedical event extraction tasks. Our method achieved state-of-the-art performance with noticeable overall F1 score improvement compared to that of existing methods for all of these corpora. Conclusions The experimental results demonstrated that our method is effective for biomedical event extraction. The combination strategy can reconstruct complex events from the output of deep neural networks, while the deep neural networks effectively capture the feature representation from the raw text. The biomedical event extraction implementation is available online at http://www.predictor.xin/event_extraction .

[1]  Sudip Kumar Naskar,et al.  Biomolecular Event Extraction using a Stacked Generalization based Classifier , 2016, ICON.

[2]  Samy Bengio,et al.  Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks , 2015, NIPS.

[3]  Sunil Kumar Sahu,et al.  Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text , 2017, CoNLL.

[4]  Dan Roth,et al.  Design Challenges and Misconceptions in Named Entity Recognition , 2009, CoNLL.

[5]  Jian Wang,et al.  Biomedical event extraction based on GRU integrating attention mechanism , 2018, BMC Bioinformatics.

[6]  Eduard H. Hovy,et al.  End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF , 2016, ACL.

[7]  Hongfei Lin,et al.  Bidirectional long short-term memory with CRF for detecting biomedical event trigger in FastText semantic space , 2018, BMC Bioinformatics.

[8]  Yaoqi Zhou,et al.  Improving protein disorder prediction by deep bidirectional long short‐term memory recurrent neural networks , 2016, Bioinform..

[9]  Noah A. Smith,et al.  Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2016, ACL 2016.

[10]  S. V. Ramanan,et al.  Performance and limitations of the linguistically motivated Cocoa/Peaberry system in a broad biological domain. , 2013, BioNLP@ACL.

[11]  Zhiyong Lu,et al.  Community challenges in biomedical text mining over 10 years: success, failure and the future , 2016, Briefings Bioinform..

[12]  Jari Björne,et al.  TEES 2.2: Biomedical Event Extraction for Diverse Corpora , 2015, BMC Bioinformatics.

[13]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[14]  Karin M. Verspoor,et al.  Optimizing graph-based patterns to extract biomedical events from the literature , 2015, BMC Bioinformatics.

[15]  Bo Xu,et al.  Joint entity and relation extraction based on a hybrid neural network , 2017, Neurocomputing.

[16]  Sampo Pyysalo,et al.  Overview of the Cancer Genetics and Pathway Curation tasks of BioNLP Shared Task 2013 , 2015, BMC Bioinformatics.

[17]  Jari Björne,et al.  EXTRACTING CONTEXTUALIZED COMPLEX BIOLOGICAL EVENTS WITH RICH GRAPH‐BASED FEATURE SETS , 2011, Comput. Intell..

[18]  Sophia Ananiadou,et al.  Adaptable, high recall, event extraction system with minimal configuration , 2015, BMC Bioinformatics.

[19]  Jari Björne,et al.  Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing , 2018, BioNLP.

[20]  Hongfei Lin,et al.  A multiple distributed representation method based on neural network for biomedical event extraction , 2017, BMC Medical Informatics and Decision Making.

[21]  Xiaolin Li,et al.  GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text , 2017, Bioinform..

[22]  Sampo Pyysalo,et al.  Event extraction across multiple levels of biological organization , 2012, Bioinform..

[23]  Joan Fisher Box,et al.  Guinness, Gosset, Fisher, and Small Samples , 1987 .

[24]  Zhiyong Lu,et al.  Best Match: New relevance search for PubMed , 2018, PLoS biology.

[25]  Lishuang Li,et al.  Extracting Biomedical Events with Parallel Multi-Pooling Convolutional Neural Networks , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[26]  Makoto Miwa,et al.  End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures , 2016, ACL.

[27]  Deyu Zhou,et al.  A semi-supervised learning framework for biomedical event extraction based on hidden topics , 2015, Artif. Intell. Medicine.

[28]  Fei Li,et al.  A transition-based model for jointly extracting drugs, diseases and adverse drug events , 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[29]  Deyu Zhou,et al.  Event trigger identification for biomedical events extraction using domain knowledge , 2014, Bioinform..

[30]  Sampo Pyysalo,et al.  EXTRACTING BIO‐MOLECULAR EVENTS FROM LITERATURE—THE BIONLP’09 SHARED TASK , 2011, Comput. Intell..

[31]  Lishuang Li,et al.  Biomedical named entity recognition based on the two channels and sentence-level reading control conditioned LSTM-CRF , 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[32]  Junichi Tsujii,et al.  Event extraction for systems biology by text mining the literature. , 2010, Trends in biotechnology.

[33]  Fei Li,et al.  A neural joint model for entity and relation extraction from biomedical text , 2017, BMC Bioinformatics.