Work

Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures

Year: 2019

Type: conference-paper

Abstract: Event factuality prediction (EFP) is the task of assessing the degree to which an event mentioned in a sentence has happened.For this task, both syntactic and semantic information are crucial to identify the important context words.The previous work for EFP has only combined these information in a simple way that cannot fully exploit their coordination.In this work, we introduce a novel graph-based neural network for EFP that can integrate the semantic and syntactic information more effectively.Our experiments demonstrate the advantage of the proposed model for EFP.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/p19-1432

Authors: Amir Pouran Ben Veyseh, Thien Huu Nguyen, Dejing Dou

Institution: University of Oregon

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Award: Phase I IUCRC University of Oregon: Center for Big Learning