How Do Your Biomedical Named Entity Recognition Models Generalize to Novel Entities?

TL;DR

This paper systematically analyzes the generalization abilities of biomedical NER models, revealing their limitations in recognizing unseen entities and proposing a debiasing method to improve their performance on novel mentions.

Contribution

It provides a comprehensive analysis of BioNER models' recognition abilities and introduces a simple debiasing technique to enhance their generalization to unseen entities.

Findings

Current models overestimate their generalization abilities.

Models struggle with recognizing novel biomedical names and synonyms.

Debiasing improves model performance on unseen mentions.

Abstract

The number of biomedical literature on new biomedical concepts is rapidly increasing, which necessitates a reliable biomedical named entity recognition (BioNER) model for identifying new and unseen entity mentions. However, it is questionable whether existing models can effectively handle them. In this work, we systematically analyze the three types of recognition abilities of BioNER models: memorization, synonym generalization, and concept generalization. We find that although current best models achieve state-of-the-art performance on benchmarks based on overall performance, they have limitations in identifying synonyms and new biomedical concepts, indicating they are overestimated in terms of their generalization abilities. We also investigate failure cases of models and identify several difficulties in recognizing unseen mentions in biomedical literature as follows: (1) models tend to exploit dataset biases, which hinders the models' abilities to generalize, and (2) several biomedical names have novel morphological patterns with weak name regularity, and models fail to recognize them. We apply a statistics-based debiasing method to our problem as a simple remedy and show the improvement in generalization to unseen mentions. We hope that our analyses and findings would be able to facilitate further research into the generalization capabilities of NER models in a domain where their reliability is of utmost importance.