Leveraging network analysis to evaluate biomedical named entity recognition tools

Abstract

The ever-growing availability of biomedical text sources has resulted in a boost in clinical studies based on their exploitation. Biomedical named-entity recognition (bio-NER) techniques have evolved remarkably in recent years and their application in research is increasingly successful. Still, the disparity of tools and the limited available validation resources are barriers preventing a wider diffusion, especially within clinical practice. We here propose the use of omics data and network analysis as an alternative for the assessment of bio-NER tools. Specifically, our method introduces quality criteria based on edge overlap and community detection. The application of these criteria to four bio-NER solutions yielded comparable results to strategies based on annotated corpora, without suffering from their limitations. Our approach can constitute a guide both for the selection of the best bio-NER tool given a specific task, and for the creation and validation of novel approaches.

Deep learning-based methods for natural hazard named entity recognition

“…When the extraction rules accurately reflect the linguistic phenomena, these methods can achieve high recognition for a specific corpus. The disadvantage is that the improvement in the recognition effect relies on many rules, is extremely dependent on artificial features, and that the rule method is complex 16 . Machine learning methods have been applied to natural hazard information extraction 17 .…”