Exploiting syntactic and semantics information for chemical–disease relation extraction

Zhou, Huiwei; Deng, Hongda; Chen, Long; Yang, Yunlong; Jia, Chen; Huang, Degen

doi:10.1093/database/baw048

Cited by 67 publications

(71 citation statements)

References 25 publications

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“…To further improve the performance, we use some postprocessing rules [11] to help extract relations when no CDR is found in a document by the CAN model. The rules are listed as follows:…”

Section: Post-processingmentioning

confidence: 99%

“…The methods mentioned above [11][12][13] take the whole sentence or the word sequence between the two target entities as input to learn semantic representations. However, for the entity pairs far away from each other, such methods may fail to describe the relation of the two entities, and some irrelevant information may also be considered due to the long distance.…”

Section: Introductionmentioning

confidence: 99%

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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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“…To further improve the performance, we use some postprocessing rules [11] to help extract relations when no CDR is found in a document by the CAN model. The rules are listed as follows:…”

Section: Post-processingmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Previous researches on biomedical relation extraction mostly focus on protein-protein interactions (1)(2)(3)(4), drug-drug interactions (6)(7)(8)(9), and chemical-disease relations (10)(11)(12)(13)(14). They can be roughly divided into three categories: rule-based methods, feature-based methods and neural network-based methods.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, deep learning techniques have achieved great success in relation extraction tasks (4,9,12,13,(16)(17)(18)(19)(20). Without feature engineering efforts, deep neural networks could effectively extract semantic information for relation extraction.…”

Section: Related Workmentioning

confidence: 99%

Leveraging prior knowledge for protein–protein interaction extraction with memory network

et al. 2018

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Automatically extracting protein–protein interactions (PPIs) from biomedical literature provides additional support for precision medicine efforts. This paper proposes a novel memory network-based model (MNM) for PPI extraction, which leverages prior knowledge about protein–protein pairs with memory networks. The proposed MNM captures important context clues related to knowledge representations learned from knowledge bases. Both entity embeddings and relation embeddings of prior knowledge are effective in improving the PPI extraction model, leading to a new state-of-the-art performance on the BioCreative VI PPI dataset. The paper also shows that multiple computational layers over an external memory are superior to long short-term memory networks with the local memories.Database URL: http://www.biocreative.org/tasks/biocreative-vi/track-4/

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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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Exploiting syntactic and semantics information for chemical–disease relation extraction

Cited by 67 publications

References 25 publications

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

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

Leveraging prior knowledge for protein–protein interaction extraction with memory network

Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction

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