Joint Event Extraction along Shortest Dependency Paths using Graph Convolutional Networks

by   Ali Balali, et al.

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.


page 7

page 13


Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation

Event extraction is of practical utility in natural language processing....

Resource-Enhanced Neural Model for Event Argument Extraction

Event argument extraction (EAE) aims to identify the arguments of an eve...

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

Implicit event argument extraction (EAE) aims to identify arguments that...

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

Event extraction (EE) is a crucial information extraction task that aims...

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

Prevalent approaches in cross-lingual relation and event extraction use ...

Event Extraction by Answering (Almost) Natural Questions

The problem of event extraction requires detecting the event trigger and...