A4 Artikkeli konferenssijulkaisussa

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




Julkaisun tekijät: Mehryary F., Moen H., Salakoski T., Ginter F.

Konferenssin vakiintunut nimi: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Kustantaja: ESANN (i6doc.com)

Julkaisuvuosi: 2020

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

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

Tietokannassa oleva lehden nimi: 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


Tiivistelmä

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.


Ladattava julkaisu

This is an electronic reprint of the original article.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.




Last updated on 2021-24-06 at 09:01