In-Order Chart-Based Constituent Parsing

TL;DR

This paper introduces an in-order chart-based model for constituent parsing that leverages in-order traversal to incorporate rich features and lookahead information, resulting in improved parsing performance.

Contribution

The novel in-order chart-based approach enhances structural knowledge encoding and outperforms previous chart-based models in constituent parsing.

Findings

Outperforms previous chart-based models on Penn Treebank

Achieves competitive results with other discriminative single models

Utilizes in-order traversal for better feature integration

Abstract

We propose a novel in-order chart-based model for constituent parsing. Compared with previous CKY-style and top-down models, our model gains advantages from in-order traversal of a tree (rich features, lookahead information and high efficiency) and makes a better use of structural knowledge by encoding the history of decisions. Experiments on the Penn Treebank show that our model outperforms previous chart-based models and achieves competitive performance compared with other discriminative single models.