Graph convolutional networks (GCNs) have a strong ability to learn graph representation
and have achieved good performance in a range of applications, including social …

This paper focuses on extracting temporal and parent–child relationships between news
events in social news. Previous methods have proved that syntactic features are valid …

Understanding events entails recognizing the structural and temporal orders between event
mentions to build event structures/graphs for input documents. To achieve this goal, our …

An important problem of Information Extraction involves Event Causality Identification (ECI)
that seeks to identify causal relation between pairs of event mentions. Prior models for ECI …

We study a new problem of cross-lingual transfer learning for event coreference resolution
(ECR) where models trained on data from a source language are adapted for evaluations in …

Event coreference resolution (ECR) aims to cluster event mentions that refer to the same
real-world events. Deep learning methods have achieved SOTA results on the ECR task …

Event causality identification (ECI) aims to identify possible causal relationships between
event-mention pairs in a text. In the past, ECI models mainly used classification frameworks …

Event relation extraction is a fundamental task in text mining, which has wide applications in
event-centric natural language processing. However, most of the existing approaches can …

Abstract What happened? Who? When? Where? Why? What will happen next? are the
fundamental questions asked to comprehend the overwhelming amount of information …

This dissertation delves into the phenomenon of pro-drop, where pronouns can be
grammatically omitted in certain languages. The research focuses on three key questions:(1) …