Joint Event Extraction along Shortest Dependency Paths using Graph Convolutional Networks

03/19/2020
by   Ali Balali, et al.
0

Event extraction (EE) is one of the core information extraction tasks, whose purpose is to automatically identify and extract information about incidents and their actors from texts. This may be beneficial to several domains such as knowledge bases, question answering, information retrieval and summarization tasks, to name a few. The problem of extracting event information from texts is longstanding and usually relies on elaborately designed lexical and syntactic features, which, however, take a large amount of human effort and lack generalization. More recently, deep neural network approaches have been adopted as a means to learn underlying features automatically. However, existing networks do not make full use of syntactic features, which play a fundamental role in capturing very long-range dependencies. Also, most approaches extract each argument of an event separately without considering associations between arguments which ultimately leads to low efficiency, especially in sentences with multiple events. To address the two above-referred problems, we propose a novel joint event extraction framework that aims to extract multiple event triggers and arguments simultaneously by introducing shortest dependency path (SDP) in the dependency graph. We do this by eliminating irrelevant words in the sentence, thus capturing long-range dependencies. Also, an attention-based graph convolutional network is proposed, to carry syntactically related information along the shortest paths between argument candidates that captures and aggregates the latent associations between arguments; a problem that has been overlooked by most of the literature. Our results show a substantial improvement over state-of-the-art methods.

READ FULL TEXT

page 7

page 13

research
09/24/2018

Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation

Event extraction is of practical utility in natural language processing....
research
10/06/2020

Resource-Enhanced Neural Model for Event Argument Extraction

Event argument extraction (EAE) aims to identify the arguments of an eve...
research
05/01/2022

CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction

Implicit event argument extraction (EAE) aims to identify arguments that...
research
03/16/2021

Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks

Existing works on information extraction (IE) have mainly solved the fou...
research
07/04/2021

CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction

Event extraction (EE) is a crucial information extraction task that aims...
research
10/06/2020

GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction

Prevalent approaches in cross-lingual relation and event extraction use ...
research
12/18/2021

Syntactic-GCN Bert based Chinese Event Extraction

With the rapid development of information technology, online platforms (...

Please sign up or login with your details

Forgot password? Click here to reset