LM-Based Word Embeddings Improve Biomedical Named Entity Recognition: A Detailed Analysis

Liliya Akhtyamova, John Cardiff

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Recent studies have shown that contextualized word embeddings outperform other types of embeddings on a variety of tasks. However, there is little research done to evaluate their effectiveness in the biomedical domain under multi-task settings. We derive the contextualized word embeddings from the Flair framework and apply them to the task of biomedical NER on 5 benchmark datasets, yielding major improvements over the baseline and achieving competitive results over the current best systems. We analyze the sources of these improvements, reporting model performances over different combinations of word embeddings, and fine-tuning and casing modes.

Original languageEnglish
Title of host publicationBioinformatics and Biomedical Engineering - 8th International Work-Conference, IWBBIO 2020, Proceedings
EditorsIgnacio Rojas, Olga Valenzuela, Fernando Rojas, Luis Javier Herrera, Francisco Ortuño
PublisherSpringer
Pages624-635
Number of pages12
ISBN (Print)9783030453848
DOIs
Publication statusPublished - 2020
Event8th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2020 - Granada, Spain
Duration: 6 May 20208 May 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12108 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2020
Country/TerritorySpain
CityGranada
Period6/05/208/05/20

Keywords

  • Biomedical named entity recognition
  • Contextualized word embeddings
  • Deep learning

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