Papers › BEEDS: Large-Scale Biomedical Event Extraction using Distant Supervision and Question Answering
BEEDS: Large-Scale Biomedical Event Extraction using Distant Supervision and Question Answering
Xing David Wang, Ulf Leser, Leon Weber
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections