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The Effect of In-Domain Word Embeddings for Chemical Named Entity Recognition

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Book cover Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1297))

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Abstract

Research articles and patents contain information in the form of text. Chemical named entity recognition (ChemNER) refers to the process of extracting chemical named entities from research articles or patents. Chemical information extraction pipelines have ChemNER as its first step. Existing ChemNER methods rely on rule-based, dictionary-based, or feature-engineered based approaches. More recently, deep learning-based approaches have been used to approach ChemNER. Deep-learning based methods utilize pre-trained word embeddings such as word2vec and Glove. Previously, we have used embedded language models (ELMo) with Bi-LSTM-CRF to learn the effect of contextual information for ChemNER. In this paper, we further experiment to learn the impact of using in-domain (large unlabelled corpora of chemical patents) pre-trained ELMo for ChemNER and compare it with ELMo pre-trained on biomedical corpora. We report the results on three benchmark corpora and conclude that in-domain embeddings statistically significantly improve F1-score on patent corpus but do not lead to any performance gains for chemical articles corpora.

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Notes

  1. 1.

    https://allennlp.org/elmo.

  2. 2.

    https://biocreative.bioinformatics.udel.edu/resources/publications/.

  3. 3.

    https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BC4CHEMD.

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Technology Sydney, Sydney, Australia

    Zainab Awan & Paul J. Kennedy

  2. Climate Change Cluster, University of Technology Sydney, Sydney, Australia

    Tim Kahlke & Peter J. Ralph

Authors
  1. Zainab Awan
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  2. Tim Kahlke
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  3. Peter J. Ralph
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  4. Paul J. Kennedy
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Corresponding author

Correspondence to Zainab Awan .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

  2. Federal University of Pernambuco, Recife, Brazil

    Ana Salgado

  3. University of Madeira, Funchal, Portugal

    David Aveiro

  4. Delft University of Technology, Delft, The Netherlands

    Jan Dietz

  5. Polytechnic Institute of Coimbra, Coimbra, Portugal

    Jorge Bernardino

  6. Polytechnic Institute of Setúbal, Setúbal, Portugal

    Joaquim Filipe

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Awan, Z., Kahlke, T., Ralph, P.J., Kennedy, P.J. (2020). The Effect of In-Domain Word Embeddings for Chemical Named Entity Recognition. In: Fred, A., Salgado, A., Aveiro, D., Dietz, J., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2019. Communications in Computer and Information Science, vol 1297. Springer, Cham. https://doi.org/10.1007/978-3-030-66196-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-66196-0_3

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-66196-0

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