Chemical-induced disease relation extraction with various linguistic features

Gu, Jinghang; Qian, Longhua; Zhou, Guodong

doi:10.1093/database/baw042

Cited by 53 publications

(83 citation statements)

References 27 publications

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“…All the instances are generated from chemical and disease mentions in a pairwise way following (18). The instances are then pooled into two groups at intra- and inter-sentence level, respectively.…”

Section: Methodsmentioning

confidence: 99%

Chemical-induced disease relation extraction via convolutional neural network

et al. 2017

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This article describes our work on the BioCreative-V chemical–disease relation (CDR) extraction task, which employed a maximum entropy (ME) model and a convolutional neural network model for relation extraction at inter- and intra-sentence level, respectively. In our work, relation extraction between entity concepts in documents was simplified to relation extraction between entity mentions. We first constructed pairs of chemical and disease mentions as relation instances for training and testing stages, then we trained and applied the ME model and the convolutional neural network model for inter- and intra-sentence level, respectively. Finally, we merged the classification results from mention level to document level to acquire the final relations between chemical and disease concepts. The evaluation on the BioCreative-V CDR corpus shows the effectiveness of our proposed approach. Database URL: http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/

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Section: Methodsmentioning

confidence: 99%

Chemical-induced disease relation extraction via convolutional neural network

et al. 2017

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“…Gu et al [6], [16], some heuristic rules are applied to construct the intra-and inter-sentence levels instances. The details of the heuristic rules are listed as follows.…”

Section: Relation Instance Constructionmentioning

confidence: 99%

Chemical-induced disease relation extraction with dependency information and prior knowledge

Zhou

Ning

Yang

et al. 2018

Journal of Biomedical Informatics

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Chemical-disease relation (CDR) extraction is significantly important to various areas of biomedical research and health care. Nowadays, many large-scale biomedical knowledge bases (KBs) containing triples about entity pairs and their relations have been built. KBs are important resources for biomedical relation extraction. However, previous research pays little attention to prior knowledge. In addition, the dependency tree contains important syntactic and semantic information, which helps to improve relation extraction. So how to effectively use it is also worth studying. In this paper, we propose a novel convolutional attention network (CAN) for CDR extraction. Firstly, we extract the shortest dependency path (SDP) between chemical and disease pairs in a sentence, which includes a sequence of words, dependency directions, and dependency relation tags. Then the convolution operations are performed on the SDP to produce deep semantic dependency features. After that, an attention mechanism is employed to learn the importance/weight of each semantic dependency vector related to knowledge representations learned from KBs. Finally, in order to combine dependency information and prior knowledge, the concatenation of weighted semantic dependency representations and knowledge representations is fed to the softmax layer for classification. Experiments on the BioCreative V CDR dataset show that our method achieves comparable performance with the state-of-the-art systems, and both dependency information and prior knowledge play important roles in CDR extraction task.

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“…Existing research on CDR extraction can be divided into two Manuscript [5] and machine learning-based [6], [7], [8], [9], [10], [11] methods. Rule-based methods aim at finding and extracting the heuristic rules for CDR extraction.…”

Section: Introductionmentioning

confidence: 99%

Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction

Zhou¹,

Yang²,

Ning³

et al. 2019

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Automatically extracting the relationships between chemicals and diseases is significantly important to various areas of biomedical research and health care. Biomedical experts have built many large-scale knowledge bases (KBs) to advance the development of biomedical research. KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in chemical-disease relation (CDR) extraction. However, previous researches pay less attention to the prior knowledge existing in KBs. This paper proposes a neural network-based attention model (NAM) for CDR extraction, which makes full use of context information in documents and prior knowledge in KBs. For a pair of entities in a document, an attention mechanism is employed to select important context words with respect to the relation representations learned from KBs. Experiments on the BioCreative V CDR dataset show that combining context and knowledge representations through the attention mechanism, could significantly improve the CDR extraction performance while achieve comparable results with state-of-the-art systems.

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Chemical-induced disease relation extraction with various linguistic features

Cited by 53 publications

References 27 publications

Chemical-induced disease relation extraction via convolutional neural network

Chemical-induced disease relation extraction via convolutional neural network

Chemical-induced disease relation extraction with dependency information and prior knowledge

Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction

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