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Efficient and Accurate Entity Recognition for Biomedical Text

Type of publication Peer-reviewed
Publikationsform Proceedings (peer-reviewed)
Author Rinaldi Fabio, Furrer Lenz, Basaldella Marco,
Project MelanoBase
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Proceedings (peer-reviewed)

Editor , Arighi Cecilia N; , Wu Cathy H; , Wang Qinghua
Page(s) 195 - 197
Title of proceedings BioCreative VI Workshop

Abstract

This short paper briefly presents an efficient implementation of a named entity recognition system for biomedical entities, which is also available as a web service. The approach is based on a dictionary-based entity recognizer combined with a machine-learning classifier which acts as a filter. We evaluated the efficiency of the approach through participation in the TIPS challenge (BioCreative V.5), where it obtained the best results among participating systems. We separately evaluated the quality of entity recognition and linking, using a manually annotated corpus as a reference (CRAFT), where we obtained state-of-the-art results.
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