Improving biomedical named entity recognition with syntactic information
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RESEARCH ARTICLE
Open Access
Improving biomedical named entity recognition with syntactic information Yuanhe Tian1†, Wang Shen2†, Yan Song3,4* , Fei Xia1, Min He2 and Kenli Li2 *Correspondence: [email protected] † Yuanhe Tian and Wang Shen contributed equally to this work 3 The Chinese University of Hong Kong, Shenzhen, China Full list of author information is available at the end of the article
Abstract Background: Biomedical named entity recognition (BioNER) is an important task for understanding biomedical texts, which can be challenging due to the lack of large-scale labeled training data and domain knowledge. To address the challenge, in addition to using powerful encoders (e.g., biLSTM and BioBERT), one possible method is to leverage extra knowledge that is easy to obtain. Previous studies have shown that auto-processed syntactic information can be a useful resource to improve model performance, but their approaches are limited to directly concatenating the embeddings of syntactic information to the input word embeddings. Therefore, such syntactic information is leveraged in an inflexible way, where inaccurate one may hurt model performance. Results: In this paper, we propose BioKMNER, a BioNER model for biomedical texts with key-value memory networks (KVMN) to incorporate auto-processed syntactic information. We evaluate BioKMNER on six English biomedical datasets, where our method with KVMN outperforms the strong baseline method, namely, BioBERT, from the previous study on all datasets. Specifically, the F1 scores of our best performing model are 85.29% on BC2GM, 77.83% on JNLPBA, 94.22% on BC5CDR-chemical, 90.08% on NCBI-disease, 89.24% on LINNAEUS, and 76.33% on Species-800, where state-of-theart performance is obtained on four of them (i.e., BC2GM, BC5CDR-chemical, NCBIdisease, and Species-800). Conclusion: The experimental results on six English benchmark datasets demonstrate that auto-processed syntactic information can be a useful resource for BioNER and our method with KVMN can appropriately leverage such information to improve model performance. Keywords: Named entity recognition, Text mining, Key-value memory networks, Syntactic information, Neural networks
Background Biomedical named entity recognition (BioNER) is an important and challenging task for understanding biomedical texts. It aims to recognize named entities (NEs), such as diseases, gene, species, etc., in biomedical texts and plays an important role in many downstream natural language processing (NLP) tasks, such as drug-drug interaction task [21, 34] and knowledge base completion [38, 47]. Compared to named entity recognition in © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other th
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