Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text

Desh Raj, Sunil Sahu, Ashish Anand


Abstract
The task of relation classification in the biomedical domain is complex due to the presence of samples obtained from heterogeneous sources such as research articles, discharge summaries, or electronic health records. It is also a constraint for classifiers which employ manual feature engineering. In this paper, we propose a convolutional recurrent neural network (CRNN) architecture that combines RNNs and CNNs in sequence to solve this problem. The rationale behind our approach is that CNNs can effectively identify coarse-grained local features in a sentence, while RNNs are more suited for long-term dependencies. We compare our CRNN model with several baselines on two biomedical datasets, namely the i2b2-2010 clinical relation extraction challenge dataset, and the SemEval-2013 DDI extraction dataset. We also evaluate an attentive pooling technique and report its performance in comparison with the conventional max pooling method. Our results indicate that the proposed model achieves state-of-the-art performance on both datasets.
Anthology ID:
K17-1032
Volume:
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
311–321
Language:
URL:
https://aclanthology.org/K17-1032
DOI:
10.18653/v1/K17-1032
Bibkey:
Cite (ACL):
Desh Raj, Sunil Sahu, and Ashish Anand. 2017. Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 311–321, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text (Raj et al., CoNLL 2017)
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PDF:
https://preview.aclanthology.org/author-url/K17-1032.pdf
Data
DDI