SynNER: syntax-infused named entity recognition in the biomedical domain.

Objective: This study evaluates the usefulness of explicit syntactic knowledge, integrated via a neural mechanism, in improving the accuracy of named entity recognition in the domain of biomedical text processing. Materials and Methods: Syntactic structure of a text can be helpful to determine whether a certain part of the text is an entity or not. Parsing is an essential technique in natural language processing (NLP) that can be utilized to determine the syntactic structure of sentences in human languages. We propose to infuse syntactic knowledge through the attention mechanism using dependency parsing and sequence labelling parsing, as well as the multi-task learning paradigm. Experiments were conducted on five datasets: MTSamples, VAERS, NCBI-disease, BC2GM, and JNLPBA. Results: We demonstrate improvements in the F1 score over the current state of the art on 3 out of 5 datasets (MTSamples, VAERS, and NCBI). Discussion: We reduce the number of mismatches with gold labels in particular in the n-dash and parentheses tokens and in compound and adjective modifier dependencies. Conclusion: Syntactic features improve NER accuracy in attention-based neural systems, and parsing as sequence labelling brings additional benefits.