Preiss, J. (2021) Predicting informativeness of semantic triples. In: Mitkov, R. and Angelova, G., (eds.) Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021). International Conference on Recent Advances in Natural Language Processing (RANLP 2021), 01-07 Sep 2021, Online. INCOMA Ltd. Shoumen, Bulgaria , pp. 1124-1129. ISBN 9789544520724
Abstract
Many automatic semantic relation extraction tools extract subject-predicate-object triples from unstructured text. However, a large quantity of these triples merely represent background knowledge. We explore using full texts of biomedical publications to create a training corpus of informative and important semantic triples based on the notion that the main contributions of an article are summarized in its abstract. This corpus is used to train a deep learning classifier to identify important triples, and we suggest that an importance ranking for semantic triples could also be generated.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2021 ACL. Licensed on a Creative Commons Attribution 4.0 International License. (https://creativecommons.org/licenses/by/4.0/) |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Apr 2024 12:13 |
Last Modified: | 16 Apr 2024 12:13 |
Status: | Published |
Publisher: | INCOMA Ltd. Shoumen, Bulgaria |
Refereed: | Yes |
Identification Number: | 10.26615/978-954-452-072-4_126 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:211374 |