Work

Graph-based Dependency Parsing with Graph Neural Networks

Year: 2019

Type: conference-paper

Abstract: We investigate the problem of efficiently incorporating high-order features into neural graph-based dependency parsing.Instead of explicitly extracting high-order features from intermediate parse trees, we develop a more powerful dependency tree node representation which captures high-order information concisely and efficiently.We use graph neural networks (GNNs) to learn the representations and discuss several new configurations of GNN's updating and aggregation functions.Experiments on PTB show that our parser achieves the best UAS and LAS on PTB (96.0%, 94.3%) among systems without using any external resources.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/p19-1237

Authors: Tao Ji, Yuanbin Wu, Man Lan

Institution: East China Normal University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Science and Technology Commission of Shanghai Municipality

OpenAlex ID: W2953359159