Comparing and combining some popular NER approaches on Biomedical tasks

Harsh Verma, Sabine Bergler, Narjesossadat Tahaei

Abstract
We compare three simple and popular approaches for NER: 1) SEQ (sequence labeling with a linear token classifier) 2) SeqCRF (sequence labeling with Conditional Random Fields), and 3) SpanPred (span prediction with boundary token embeddings). We compare the approaches on 4 biomedical NER tasks: GENIA, NCBI-Disease, LivingNER (Spanish), and SocialDisNER (Spanish). The SpanPred model demonstrates state-of-the-art performance on LivingNER and SocialDisNER, improving F1 by 1.3 and 0.6 F1 respectively. The SeqCRF model also demonstrates state-of-the-art performance on LivingNER and SocialDisNER, improving F1 by 0.2 F1 and 0.7 respectively. The SEQ model is competitive with the state-of-the-art on LivingNER dataset. We explore some simple ways of combining the three approaches. We find that majority voting consistently gives high precision and high F1 across all 4 datasets. Lastly, we implement a system that learns to combine SEQ’s and SpanPred’s predictions, generating systems that give high recall and high F1 across all 4 datasets. On the GENIA dataset, we find that our learned combiner system significantly boosts F1(+1.2) and recall(+2.1) over the systems being combined.
Anthology ID:
2023.bionlp-1.24
Volume:
The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Dina Demner-fushman, Sophia Ananiadou, Kevin Cohen
Venue:
BioNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
273–279
Language:
URL:
https://aclanthology.org/2023.bionlp-1.24
DOI:
10.18653/v1/2023.bionlp-1.24
Bibkey:
Cite (ACL):
Harsh Verma, Sabine Bergler, and Narjesossadat Tahaei. 2023. Comparing and combining some popular NER approaches on Biomedical tasks. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 273–279, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Comparing and combining some popular NER approaches on Biomedical tasks (Verma et al., BioNLP 2023)
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PDF:
https://preview.aclanthology.org/nschneid-patch-5/2023.bionlp-1.24.pdf
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