Refereed article in conference proceedings (A4)

Entity-pair embeddings for improving relation extraction in the biomedical domain




List of Authors: Mehryary F., Moen H., Salakoski T., Ginter F.

Conference name: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Publisher: ESANN (i6doc.com)

Publication year: 2020

Journal: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Book title *: ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Journal name in source: ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

ISBN: 978-2-87587-073-5

eISBN: 978-2-87587-074-2

Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/51293362


Abstract

We introduce a new approach for training named-entity pair embeddings to improve relation extraction performance in the biomedical domain. These embeddings are trained in
an unsupervised manner, based on the principles of distributional
semantics. By adding them to neural network architectures, we show that
improved F-Scores are achieved. Our best performing neural model which
utilizes entity-pair embeddings along with a pre-trained BERT encoder, achieves an F-score of 77.19 on CHEMPROT (Chemical-Protein) relation extraction corpus, setting a new state-of-the-art result for the task.


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Last updated on 2022-07-04 at 18:17