DeepEventMine: end-to-end neural nested event extraction from biomedical texts.

ABSTRACT:

Motivation

Recent 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.

Results

We 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 e

SUBMITTER: Trieu HL 

PROVIDER: S-EPMC7750964 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature