ArticleBioinformatics (Oxford, England)2020
DeepEventMine: end-to-end neural nested event extraction from biomedical texts.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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Who cites it
13 citing papers in PubMed.
- Effective type label-based synergistic representation learning for biomedical event trigger detection.BMC bioinformatics · 2024Article
- Comparing generative and extractive approaches to information extraction from abstracts describing randomized clinical trials.Journal of biomedical semantics · 2024Article
- Predicting potential target genes in molecular biology experiments using machine learning and multifaceted data sources.iScience · 2024Article
- Grammar-constrained decoding for structured information extraction with fine-tuned generative models applied to clinical trial abstracts.Frontiers in artificial intelligence · 2024Article
- GePI: large-scale text mining, customized retrieval and flexible filtering of gene/protein interactions.Nucleic acids research · 2023Article
- Fine-tuning large neural language models for biomedical natural language processing.Patterns (New York, N.Y.) · 2023Article
- A novel corpus of molecular to higher-order events that facilitates the understanding of the pathogenic mechanisms of idiopathic pulmonary fibrosis.Scientific reports · 2023Article
- A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.NPJ digital medicine · 2022Review
- Conditional Probability Joint Extraction of Nested Biomedical Events: Design of a Unified Extraction Framework Based on Neural Networks.JMIR medical informatics · 2022Article
- Comparing neural models for nested and overlapping biomedical event detection.BMC bioinformatics · 2022Article
- Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature.Sensors (Basel, Switzerland) · 2021Article
- MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra.Biomolecules · 2021Article
- AT-NeuroEAE: A Joint Extraction Model of Events With Attributes for Research Sharing-Oriented Neuroimaging Provenance Construction.Frontiers in neuroscience · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
motivationRecent neural approaches on event extraction from text mainly focus on flat events in general domain, while there are less attempts to detect nested and overlapping events. These existing systems are built on given entities and they depend on external syntactic tools.
resultsWe propose an end-to-end neural nested event extraction model named DeepEventMine that extracts multiple overlapping directed acyclic graph structures from a raw sentence. On the top of the bidirectional encoder representations from transformers model, our model detects nested entities and triggers, roles, nested events and their modifications in an end-to-end manner without any syntactic tools. Our DeepEventMine model achieves the new state-of-the-art performance on seven biomedical nested event extraction tasks. Even when gold entities are unavailable, our model can detect events from raw text with promising performance. AVAILABILITY AND IMPLEMENTATION: Our codes and models to reproduce the results are available at: https://github.com/aistairc/DeepEventMine. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.