Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on sleep in brain health

LIVE: Thursday 2nd May 2024, 18:00-19:30 (CEST)

Quality sleep is essential for health. But what happens to our brains when sleep patterns are disturbed? Join our experts to explore the interplay between sleep disruption and neurological diseases, and the questions that you need to be asking your patients to help you prevent the harmful effects of sleep deprivation.
ADVANCED SEARCH
Log in
Top
BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 3/2017

Open Access 01-12-2017 | Research

A multiple distributed representation method based on neural network for biomedical event extraction

Authors: Anran Wang, Jian Wang, Hongfei Lin, Jianhai Zhang, Zhihao Yang, Kan Xu

Published in: BMC Medical Informatics and Decision Making | Special Issue 3/2017

Login to get access

Abstract

Background

Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words.

Methods

In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates.

Results

The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method.

Conclusions

Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction.
Literature
1.
2.
Riedel S, Mcclosky D, Surdeanu M, et al. Model combination for event extraction in BioNLP 2011[C]. Proceedings of BioNLP Shared Task 2011 Workshop, 2011:51-55.
3.
Zhou D, Zhong D. A semi-supervised learning framework for biomedical event extraction based on hidden topics.[J]. Artif Intell Med. 2015;64(1):51–8.CrossRefPubMed
4.
Miwa M, Thompson P, Ananiadou S. Boosting automatic event extraction from the literature using domain adaptation and coreference resolution[J]. Bioinformatics. 2012;28(13):1759–65.CrossRefPubMedPubMedCentral
5.
Riedel S, Mccallum A. Fast and robust joint models for biomedical event extraction[C]. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. 2011:1-12.
6.
Kai H, Landeghem SV, Salakoski T, et al. EVEX in ST'13: application of a large-scale text mining resource to event extraction and network construction[C]. Proceedings of the BioNLP Shared Task 2013 Workshop. 2013:26–34.
7.
Björne J, Salakoski T. TEES 2.1: automated annotation scheme learning in the BioNLP 2013 shared task[C]. Proceedings of the BioNLP Shared Task 2013 Workshop. 2013:16–25.
8.
Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality[J]. Advances in Neural Information Processing Systems. 2013(26): 3111–19.
9.
Tang B, Cao H, Wang X, et al. Evaluating word representation features in biomedical named entity recognition tasks.[J]. Biomed Res Int. 2014;2014(2):240403.PubMedPubMedCentral
10.
Ma M, Huang L, Zhou B, Xiang B. Dependency-based Convolutional neural networks for sentence embedding[C]. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers); 2015: p174–179.
11.
Sagae K, Tsujii JI. Dependency parsing and domain adaptation with LR models and parser ensembles[C]. Proceedings of the CoNLL Shared Task Session of EMNLP-CoNLL. 2007:1044-50.
12.
Levy O, Goldberg Y. Dependency-based word embeddings[C]. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2014:302–308.
13.
Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation[J]. J Mach Learn Res. 2003;3:993–1022.
14.
Mikolov T, Zweig G. Context dependent recurrent neural network language model[C]. Proceedings of the Spoken Language Technology Workshop, IEEE. 2012:234-239.
15.
Wang J, Zhang J, An Y, et al. Biomedical event trigger detection by dependency-based word embedding[J]. BMC Medical Genomics. 2016, vol 9,Suppl 2: 45.
16.
Bergstra J, Breuleux O, Bastien F, et al. Theano: a CPU and GPU math compiler in python[C]. Proceedings of the 9th Python in Science Conf. 2010:1–7.
17.
Zeiler M D. ADADELTA: an adaptive learning rate method[J]. arXiv preprint arXiv.2012:1212.5701.
18.
Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. J Mach Learn Res. 2014;15(1):1929–58.
Metadata
Title
A multiple distributed representation method based on neural network for biomedical event extraction
Authors
Anran Wang
Jian Wang
Hongfei Lin
Jianhai Zhang
Zhihao Yang
Kan Xu
Publication date
01-12-2017
Publisher
BioMed Central
DOI
https://doi.org/10.1186/s12911-017-0563-9

Other articles of this Special Issue 3/2017

BMC Medical Informatics and Decision Making 3/2017 Go to the issue

Research

Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM

Research

Subject-independent emotion recognition based on physiological signals: a three-stage decision method

Research

Surface structure feature matching algorithm for cardiac motion estimation

Research

Substance deposition assessment in obstructed pulmonary system through numerical characterization of airflow and inhaled particles attributes