TY - GEN
T1 - GGNN@Causal News Corpus 2022
T2 - 5th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, CASE 2022
AU - Trust, Paul
AU - Minghim, Rosane
AU - Provia, Kadusabe
AU - Millos, Evangelos
N1 - Publisher Copyright:
© 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85152924775
M3 - Conference proceeding
AN - SCOPUS:85152924775
T3 - CASE 2022 - 5th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, Proceedings of the Workshop
SP - 85
EP - 90
BT - CASE 2022 - 5th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, Proceedings of the Workshop
PB - Association for Computational Linguistics (ACL)
Y2 - 7 December 2022 through 8 December 2022
ER -