Improved Biomedical Entity Recognition via Longer Context Modeling - Artificial Intelligence Applications and Innovations Access content directly
Conference Papers Year : 2021

Improved Biomedical Entity Recognition via Longer Context Modeling

Abstract

Biomedical Named Entity Recognition is a difficult task, aimed to identify all named entities in medical literature. The importance of the task becomes apparent as these entities are used to identify key features, enable better search results and can accelerate the process of reviewing related evidence to a medical case. This practice is known as Evidence-Based Medicine (EBM) and is globally used by medical practitioners who do not have the time to read all the latest developments in their respective fields. In this paper we propose a methodology which achieves state-of-the-art results in a plethora of Biomedical Named Entity Recognition datasets, with a lightweight approach that requires minimal training. Our model is end-to-end and capable of efficiently modeling significantly longer sequences than previous models, benefiting from inter-sentence dependencies.
Fichier principal
Vignette du fichier
509922_1_En_4_Chapter.pdf (315.52 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03287680 , version 1 (15-07-2021)

Licence

Attribution

Identifiers

Cite

Nikolaos Stylianou, Panagiotis Kosmoliaptsis, Ioannis Vlahavas. Improved Biomedical Entity Recognition via Longer Context Modeling. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.45-56, ⟨10.1007/978-3-030-79150-6_4⟩. ⟨hal-03287680⟩
26 View
27 Download

Altmetric

Share

Gmail Facebook X LinkedIn More