HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools

TL;DR

This study evaluates the real-world performance of biomedical named entity recognition and normalization tools across different datasets, revealing significant performance drops outside their training contexts and highlighting the need for more robust tools.

Contribution

It provides a comprehensive cross-corpus benchmark of 28 systems, analyzing their robustness and performance variability in real-world biomedical text mining applications.

Findings

Performance drops significantly in cross-corpus settings

HunFlair2 outperforms other tools on average

Tool performance is less reliable outside original training data

Abstract

With the exponential growth of the life science literature, biomedical text mining (BTM) has become an essential technology for accelerating the extraction of insights from publications. Identifying named entities (e.g., diseases, drugs, or genes) in texts and their linkage to reference knowledge bases are crucial steps in BTM pipelines to enable information aggregation from different documents. However, tools for these two steps are rarely applied in the same context in which they were developed. Instead, they are applied in the wild, i.e., on application-dependent text collections different from those used for the tools' training, varying, e.g., in focus, genre, style, and text type. This raises the question of whether the reported performance of BTM tools can be trusted for downstream applications. Here, we report on the results of a carefully designed cross-corpus benchmark for named entity extraction, where tools were applied systematically to corpora not used during their training. Based on a survey of 28 published systems, we selected five for an in-depth analysis on three publicly available corpora encompassing four different entity types. Comparison between tools results in a mixed picture and shows that, in a cross-corpus setting, the performance is significantly lower than the one reported in an in-corpus setting. HunFlair2 showed the best performance on average, being closely followed by PubTator. Our results indicate that users of BTM tools should expect diminishing performances when applying them in the wild compared to original publications and show that further research is necessary to make BTM tools more robust.