Abstract
Automatic extraction of event structures from text is a promising way to extract important facts from the evergrowing amount of biomedical literature. We propose BEEDS, a new approach on how to mine event structures from PubMed based on a question-answering paradigm. Using a three-step pipeline comprising a document retriever, a document reader, and an entity normalizer, BEEDS is able to fully automatically extract event triples involving a query protein or gene and to store this information directly in a knowledge base. BEEDS applies a transformer-based architecture for event extraction and uses distant supervision to augment the scarce training data in event mining. In a knowledge base population setting, it outperforms a strong baseline in finding post-translational modification events consisting of enzyme-substrate-site triples while achieving competitive results in extracting binary relations consisting of protein-protein and protein-site interactions.- Anthology ID:
- 2022.bionlp-1.28
- Volume:
- Proceedings of the 21st Workshop on Biomedical Language Processing
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 298–309
- Language:
- URL:
- https://aclanthology.org/2022.bionlp-1.28
- DOI:
- 10.18653/v1/2022.bionlp-1.28
- Cite (ACL):
- Xing David Wang, Ulf Leser, and Leon Weber. 2022. BEEDS: Large-Scale Biomedical Event Extraction using Distant Supervision and Question Answering. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 298–309, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- BEEDS: Large-Scale Biomedical Event Extraction using Distant Supervision and Question Answering (Wang et al., BioNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.bionlp-1.28.pdf
- Code
- wangxii/beeds