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Combining dictionary- and rule-based approximate entity linking with tuned BioBERT

View ORCID ProfileGhadeer Mobasher, Lukrécia Mertová, Sucheta Ghosh, Olga Krebs, Bettina Heinlein, Wolfgang Müller
doi: https://doi.org/10.1101/2021.11.09.467905
Ghadeer Mobasher
Scientific Databases and Visualization Group (SDBV), Heidelberg Institute for Theoretical Studies – HITS gGmbH, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
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  • ORCID record for Ghadeer Mobasher
  • For correspondence: ghadeer.mobasher@h-its.org
Lukrécia Mertová
Scientific Databases and Visualization Group (SDBV), Heidelberg Institute for Theoretical Studies – HITS gGmbH, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
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Sucheta Ghosh
Scientific Databases and Visualization Group (SDBV), Heidelberg Institute for Theoretical Studies – HITS gGmbH, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
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Olga Krebs
Scientific Databases and Visualization Group (SDBV), Heidelberg Institute for Theoretical Studies – HITS gGmbH, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
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Bettina Heinlein
Scientific Databases and Visualization Group (SDBV), Heidelberg Institute for Theoretical Studies – HITS gGmbH, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
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Wolfgang Müller
Scientific Databases and Visualization Group (SDBV), Heidelberg Institute for Theoretical Studies – HITS gGmbH, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
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Abstract

Chemical named entity recognition (NER) is a significant step for many downstream applications like entity linking for the chemical text-mining pipeline. However, the identification of chemical entities in a biomedical text is a challenging task due to the diverse morphology of chemical entities and the different types of chemical nomenclature. In this work, we describe our approach that was submitted for BioCreative version 7 challenge Track 2, focusing on the ‘Chemical Identification’ task for identifying chemical entities and entity linking, using MeSH. For this purpose, we have applied a two-stage approach as follows (a) usage of fine-tuned BioBERT for identification of chemical entities (b) semantic approximate search in MeSH and PubChem databases for entity linking. There was some friction between the two approaches, as our rule-based approach did not harmonise optimally with partially recognized words forwarded by the BERT component. For our future work, we aim to resolve the issue of the artefacts arising from BERT tokenizers and develop joint learning of chemical named entity recognition and entity linking using pre-trained transformer-based models and compare their performance with our preliminary approach. Next, we will improve the efficiency of our approximate search in reference databases during entity linking. This task is non-trivial as it entails determining similarity scores of large sets of trees with respect to a query tree. Ideally, this will enable flexible parametrization and rule selection for the entity linking search.

Competing Interest Statement

Ghadeer Mobasher is part of the PoLiMeR-ITN (http://polimer-itn.eu/). Her project is supported by European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement PoLiMeR, No 81261. The work was supported by the Klaus Tschira Foundation, KTS.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 11, 2021.
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Combining dictionary- and rule-based approximate entity linking with tuned BioBERT
Ghadeer Mobasher, Lukrécia Mertová, Sucheta Ghosh, Olga Krebs, Bettina Heinlein, Wolfgang Müller
bioRxiv 2021.11.09.467905; doi: https://doi.org/10.1101/2021.11.09.467905
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Combining dictionary- and rule-based approximate entity linking with tuned BioBERT
Ghadeer Mobasher, Lukrécia Mertová, Sucheta Ghosh, Olga Krebs, Bettina Heinlein, Wolfgang Müller
bioRxiv 2021.11.09.467905; doi: https://doi.org/10.1101/2021.11.09.467905

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