Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction

by   Tianze Shi, et al.

We propose a transition-based bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously. Bubble representations were proposed in the formal linguistics literature decades ago; they enhance dependency trees by encoding coordination boundaries and internal relationships within coordination structures explicitly. In this paper, we introduce a transition system and neural models for parsing these bubble-enhanced structures. Experimental results on the English Penn Treebank and the English GENIA corpus show that our parsers beat previous state-of-the-art approaches on the task of coordination structure prediction, especially for the subset of sentences with complex coordination structures.


page 1

page 2

page 3

page 4


Improved Parsing for Argument-Clusters Coordination

Syntactic parsers perform poorly in prediction of Argument-Cluster Coord...

A Neural Network for Coordination Boundary Prediction

We propose a neural-network based model for coordination boundary predic...

Coordination Annotation Extension in the Penn Tree Bank

Coordination is an important and common syntactic construction which is ...

Enacting Coordination Processes

With the rise of data-centric process management paradigms, interdepende...

OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction

A recent state-of-the-art neural open information extraction (OpenIE) sy...

An In-depth Study on Internal Structure of Chinese Words

Unlike English letters, Chinese characters have rich and specific meanin...

Semistability-Based Convergence Analysis for Paracontracting Multiagent Coordination Optimization

This sequential technical report extends some of the previous results we...