Comparing and combining some popular NER approaches on Biomedical tasks
Harsh Verma, Sabine Bergler, Narjes Tahaei · 2023
We compare three simple and popular approaches for NER: 1) SEQ (sequencelabeling with a linear token classifier) 2) SeqCRF (sequence-labeling with Conditional Random Fields), and 3) SpanPred (spanprediction with boundary token embeddings).We compare the approaches on 4 biomedical NER tasks:GENIA, NCBI-Disease, LivingNER (Spanish), 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 the 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 the predictions of SEQ and SpanPred, generating systems that consistently 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.We release all the well-documented code necessary to reproduce all systems at this Github repository.