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Related Experiment Video

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Comparing general and specialized word embeddings for biomedical named entity recognition.

Rigo E Ramos-Vargas1, Israel Román-Godínez1, Sulema Torres-Ramos1

  • 1Departamento de Ciencias Computacionales, Universidad de Guadalajara, Guadalajara, Jalisco, México.

Peerj. Computer Science
|April 5, 2021
PubMed
Summary

General word embeddings can outperform specialized ones for classic biomedical named entity recognition (BioNER) tasks. However, specific word embeddings are superior for contextualized BioNER, offering better performance in biomedical literature analysis.

Keywords:
BiLSTM-CRFBioNERDrugBankELMo embeddingsGlove common crawlMedLinePooled flair embeddingsPyysalo PM + PMCTransformer embeddingsWord embeddings

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Biomedical Named Entity Recognition (BioNER) benefits from word embeddings.
  • Selecting optimal word embeddings requires careful evaluation.
  • General vs. specific word embeddings for BioNER is an underexplored area.

Purpose of the Study:

  • Evaluate classic and contextualized word embeddings for BioNER.
  • Compare general and specific word embeddings.
  • Determine optimal embedding strategies for BioNER tasks.

Main Methods:

  • Tested three NER algorithms (CRF, BiLSTM, BiLSTM-CRF) with classic embeddings (GloVe, Pyysalo).
  • Compared general and specific versions of contextualized embeddings (ELMo, Flair, Transformer).
  • Evaluated performance across DrugBank and MedLine corpora.

Main Results:

  • General classic word embedding (GloVe) outperformed specific (Pyysalo) on the DrugBank corpus.
  • Specific contextualized word embeddings yielded the best results.
  • Classic specific embeddings showed better coverage and semantic relationships.

Conclusions:

  • General classic word embeddings are a viable option for BioNER.
  • Specific contextualized word embeddings are the preferred choice for BioNER.
  • Embedding choice significantly impacts BioNER performance.