A Byte-sized Approach to Named Entity Recognition

TL;DR

This paper presents a novel byte-level NER method using byte-pair encodings combined with neural networks, effectively identifying biomedical entities without relying on domain-specific tokenization rules.

Contribution

The authors introduce a subword-based NER approach that operates at the byte level, bypassing the need for specialized tokenization in biomedical texts.

Findings

Achieved competitive results on biomedical NER datasets.

Eliminated dependency on domain-specific tokenization rules.

Demonstrated effectiveness of byte-level modeling in NER.

Abstract

In biomedical literature, it is common for entity boundaries to not align with word boundaries. Therefore, effective identification of entity spans requires approaches capable of considering tokens that are smaller than words. We introduce a novel, subword approach for named entity recognition (NER) that uses byte-pair encodings (BPE) in combination with convolutional and recurrent neural networks to produce byte-level tags of entities. We present experimental results on several standard biomedical datasets, namely the BioCreative VI Bio-ID, JNLPBA, and GENETAG datasets. We demonstrate competitive performance while bypassing the specialized domain expertise needed to create biomedical text tokenization rules.