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.- Anthology ID:
- 2021.ranlp-1.126
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
- Month:
- September
- Year:
- 2021
- Address:
- Held Online
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 1124–1129
- Language:
- URL:
- https://aclanthology.org/2021.ranlp-1.126
- DOI:
- Cite (ACL):
- Judita Preiss. 2021. Predicting Informativeness of Semantic Triples. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1124–1129, Held Online. INCOMA Ltd..
- Cite (Informal):
- Predicting Informativeness of Semantic Triples (Preiss, RANLP 2021)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2021.ranlp-1.126.pdf
- Data
- CORD-19