@inbook{1f5c3e78fad54cbdb9c01dede04ddff8,
title = "GGNN@Causal News Corpus 2022: Gated Graph Neural Networks for Event Causality Identification from Social-Political News Articles",
abstract = "The discovery of causality mentions from text is a core cognitive concept and appears in many natural language processing (NLP) applications. In this paper, we study the task of Event Causality Identification (ECI) from social-political news. The aim of the task is to detect causal relationships between event mention pairs in text. Although deep learning models have recently achieved a state-of-the-art performance on many tasks and applications in NLP, most of them still fail to capture rich semantic and syntactic structures within sentences which is key for causality classification. We present a solution for causal event detection from social-political news that captures semantic and syntactic information based on gated graph neural networks (GGNN) and contextualized language embeddings. Experimental results show that our proposed method outperforms the baseline model (BERT (Bidirectional Embeddings from Transformers) in terms of f1-score and accuracy.",
author = "Paul Trust and Rosane Minghim and Kadusabe Provia and Evangelos Millos",
note = "Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; 5th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, CASE 2022 ; Conference date: 07-12-2022 Through 08-12-2022",
year = "2022",
language = "English",
series = "CASE 2022 - 5th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, Proceedings of the Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "85--90",
booktitle = "CASE 2022 - 5th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, Proceedings of the Workshop",
address = "Australia",
}