Abstract
Understanding chemical-disease relations (CDR) from biomedical literature is important for biomedical research and chemical discovery. This paper uses a k-max pooling convolutional neural network (CNN) to exploit word sequences and dependency structures for CDR extraction. Furthermore, an effective weighted context method is proposed to capture semantic information of word sequences. Our system extracts both intra- and inter-sentence level chemical-disease relations, which are merged as the final CDR. Experiments on the BioCreative V CDR dataset show that both word sequences and dependency structures are effective for CDR extraction, and their integration could further improve the extraction performance.
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This research is supported by Natural Science Foundation of China (No. 61272375).
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Zhou, H., Yang, Y., Liu, Z., Liu, Z., Men, Y. (2017). Integrating Word Sequences and Dependency Structures for Chemical-Disease Relation Extraction. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2017 2017. Lecture Notes in Computer Science(), vol 10565. Springer, Cham. https://doi.org/10.1007/978-3-319-69005-6_9
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DOI: https://doi.org/10.1007/978-3-319-69005-6_9
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