Chemical-induced disease relation extraction via convolutional neural network
By Jinghang Gu, Fuqing Sun, Longhua Qian, Guodong Zhou. 2017
97 citations recorded by the source corpus.
Abstract 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/
Subfields: Artificial Intelligence, Molecular Biology
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In Artificial Intelligence
- Wei Dang Lu
- Guoshun Nan
- Zhijiang Guo
- Ivan Sekulic
- Bryan Perozzi
- Shirui Pan
- Chengqi Zhang
- William L. Hamilton
In Molecular Biology
Citations in the corpus
- Reasoning with Latent Structure Refinement for Document-Level Relation Extraction (2020, background): “Gu et al. (2017); Nguyen and Verspoor (2018 3, our LSR performs worse than the state-of-the-art models.”