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We present a simple approach to improve biomedical named entity recognition(NER) by injecting categorical labels and Part-of-speech (POS) information intothe model. We use two approaches, in the first approach, we first train asequence-level classifier to classify the sentences into categories to obtainthe sentence-level tags (categorical labels). The sequence classifier ismodeled as an entailment problem by modifying the labels as a natural languagetemplate. This helps to improve the accuracy of the classifier. Further, thislabel information is injected into the NER model. In this paper, we demonstrateeffective ways to represent and inject these labels and POS attributes into theNER model. In the second approach, we jointly learn the categorical labels andNER labels. Here we also inject the POS tags into the model to increase thesyntactic context of the model. Experiments on three benchmark datasets showthat incorporating categorical label information with syntactic context isquite useful and outperforms baseline BERT-based models.

Injecting Categorical Labels and Syntactic Information into Biomedical NER