Predicting informativeness of semantic triples

Preiss, J ORCID: 2021, Predicting informativeness of semantic triples , in: Recent Advances in Natural Language Processing 2021 (RANLP 2021), 1st-3rd September 2021, Online.

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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.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Proceedings of the International Conference on Recent Advances in Natural Language Processing
Publisher: ACL Anthology
Related URLs:
Funders: ERDF Greater Manchester AI Foundry grant
Depositing User: J Preiss
Date Deposited: 24 Nov 2021 16:13
Last Modified: 24 Nov 2021 16:15

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