Supervised Grammar Induction Using Training Data with Limited Constituent Information

TL;DR

This paper proposes an adaptation strategy for grammar induction that effectively utilizes limited higher-level constituent labels, enabling the creation of parsers that perform comparably to those trained on fully annotated data.

Contribution

It introduces a novel approach to grammar induction from sparsely labeled data by focusing on higher-level constituents, reducing annotation costs.

Findings

Induction from higher-level labels yields near full-data performance.

Higher nodes in parse trees are the most informative constituents.

Partial parsers should focus on higher-level constituents for effective grammar induction.

Abstract

Corpus-based grammar induction generally relies on hand-parsed training data to learn the structure of the language. Unfortunately, the cost of building large annotated corpora is prohibitively expensive. This work aims to improve the induction strategy when there are few labels in the training data. We show that the most informative linguistic constituents are the higher nodes in the parse trees, typically denoting complex noun phrases and sentential clauses. They account for only 20% of all constituents. For inducing grammars from sparsely labeled training data (e.g., only higher-level constituent labels), we propose an adaptation strategy, which produces grammars that parse almost as well as grammars induced from fully labeled corpora. Our results suggest that for a partial parser to replace human annotators, it must be able to automatically extract higher-level constituents rather than base noun phrases.