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

## TL;DR

This paper introduces SynNER, a method that uses syntax to improve biomedical named entity recognition, achieving better accuracy on several datasets.

## Contribution

The novel integration of explicit syntactic knowledge via attention mechanisms and multi-task learning in biomedical NER.

## Key findings

- SynNER improves F1 scores over state-of-the-art methods on three biomedical datasets.
- The method reduces mismatches in handling tokens like n-dash and parentheses, and syntactic dependencies.
- Parsing as sequence labeling provides additional benefits to NER accuracy.

## Abstract

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.

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.

We demonstrate improvements in the F1 score over the current state of the art on 3 out of 5 datasets (MTSamples, VAERS, and NCBI).

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.

Syntactic features improve NER accuracy in attention-based neural systems, and parsing as sequence labelling brings additional benefits.