Learning Entity-Likeness with Multiple Approximate Matches for Biomedical NER

An Nguyen Le, Hajime Morita, Tomoya Iwakura


Abstract
Biomedical Named Entities are complex, so approximate matching has been used to improve entity coverage. However, the usual approximate matching approach fetches only one matching result, which is often noisy. In this work, we propose a method for biomedical NER that fetches multiple approximate matches for a given phrase to leverage their variations to estimate entity-likeness. The model uses pooling to discard the unnecessary information from the noisy matching results, and learn the entity-likeness of the phrase with multiple approximate matches. Experimental results on three benchmark datasets from the biomedical domain, BC2GM, NCBI-disease, and BC4CHEMD, demonstrate the effectiveness. Our model improves the average by up to +0.21 points compared to a BioBERT-based NER.
Anthology ID:
2021.ranlp-1.117
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1040–1049
Language:
URL:
https://aclanthology.org/2021.ranlp-1.117
DOI:
Bibkey:
Cite (ACL):
An Nguyen Le, Hajime Morita, and Tomoya Iwakura. 2021. Learning Entity-Likeness with Multiple Approximate Matches for Biomedical NER. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1040–1049, Held Online. INCOMA Ltd..
Cite (Informal):
Learning Entity-Likeness with Multiple Approximate Matches for Biomedical NER (Nguyen Le et al., RANLP 2021)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/2021.ranlp-1.117.pdf
Data
CoNLL 2003