Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task

Wei, Chih-Hsuan; Peng, Yifan; Leaman, Robert; Davis, Allan Peter; Mattingly, Carolyn J.; Li, Jiao; Wiegers, Thomas C.; Lu, Zhiyong

doi:10.1093/database/baw032

Cited by 152 publications

(134 citation statements)

References 27 publications

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“…Unlike other relation corpora (17, 18), cross-sentence relations are rare in this corpus, appearing in <1% of the training set. We also noticed that some chemical–protein pairs have multiple labels, but they only appear less than ten times in the training set.…”

Section: Methodsmentioning

confidence: 81%

Extracting chemical–protein relations with ensembles of SVM and deep learning models

et al. 2018

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Mining relations between chemicals and proteins from the biomedical literature is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of systems that can automatically detect the chemical–protein relations in running text (PubMed abstracts). This work describes our CHEMPROT track entry, which is an ensemble of three systems, including a support vector machine, a convolutional neural network, and a recurrent neural network. Their output is combined using majority voting or stacking for final predictions. Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall for an F-score of 0.6410 during the challenge, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from biomedical literature and achieving the highest performance in the task during the 2017 challenge.Database URL: http://www.biocreative.org/tasks/biocreative-vi/track-5/

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Section: Methodsmentioning

confidence: 81%

Extracting chemical–protein relations with ensembles of SVM and deep learning models

et al. 2018

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“…In the biomedical domain, various relation extraction tasks such as protein–protein interactions (26, 27), drug–drug interactions (28) and chemical–disease interactions (29) have been investigated in prior shared tasks in the biomedical domain. Various machine learning-based methods including supervised machine learning methods (30, 31), pattern clustering (32) and topic modeling (33) were used before deep learning models became dominant among the recent advances.…”

Section: Related Workmentioning

confidence: 99%

Extracting chemical–protein relations using attention-based neural networks

et al. 2018

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Relation extraction is an important task in the field of natural language processing. In this paper, we describe our approach for the BioCreative VI Task 5: text mining chemical–protein interactions. We investigate multiple deep neural network (DNN) models, including convolutional neural networks, recurrent neural networks (RNNs) and attention-based (ATT-) RNNs (ATT-RNNs) to extract chemical–protein relations. Our experimental results indicate that ATT-RNN models outperform the same models without using attention and the ATT-gated recurrent unit (ATT-GRU) achieves the best performing micro average F1 score of 0.527 on the test set among the tested DNNs. In addition, the result of word-level attention weights also shows that attention mechanism is effective on selecting the most important trigger words when trained with semantic relation labels without the need of semantic parsing and feature engineering. The source code of this work is available at https://github.com/ohnlp/att-chemprot.

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“…Many systems for disease and chemical entity recognition from text were developed (1, 2). Some of these relied on biomedical dictionaries.…”

Section: Introductionmentioning

confidence: 99%

“…The chemical-disease relation (CDR) task (1) in BioCreative V aims to encourage the further development of techniques for recognizing chemical and disease entities and detecting the CDRs. Disease named entity recognition (DNER) and normalization is an intermediate step before the CDR extraction.…”

Section: Introductionmentioning

confidence: 99%

Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks

Wei¹,

Chen²,

Xu³

et al. 2016

Database

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The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available online such as ChemSpot and tmChem, the publicly available recognition systems of disease named entities are rare. This article presents a system for disease named entity recognition (DNER) and normalization. First, two separate DNER models are developed. One is based on conditional random fields model with a rule-based post-processing module. The other one is based on the bidirectional recurrent neural networks. Then the named entities recognized by each of the DNER model are fed into a support vector machine classifier for combining results. Finally, each recognized disease named entity is normalized to a medical subject heading disease name by using a vector space model based method. Experimental results show that using 1000 PubMed abstracts for training, our proposed system achieves an F1-measure of 0.8428 at the mention level and 0.7804 at the concept level, respectively, on the testing data of the chemical-disease relation task in BioCreative V.Database URL: http://219.223.252.210:8080/SS/cdr.html

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Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task

Cited by 152 publications

References 27 publications

Extracting chemical–protein relations with ensembles of SVM and deep learning models

Extracting chemical–protein relations with ensembles of SVM and deep learning models

Extracting chemical–protein relations using attention-based neural networks

Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks

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