Using existing systems to supplement small amounts of annotated grammatical relations training data

TL;DR

This paper explores leveraging existing NLP systems to enhance the training of grammatical relation classifiers when only limited annotated data is available, improving performance in natural language processing tasks.

Contribution

It introduces a method to use existing systems' outputs to supplement small annotated datasets for better grammatical relation learning.

Findings

Improved accuracy with limited annotated data

Effective use of existing systems for data augmentation

Enhanced performance in grammatical relation extraction

Abstract

Grammatical relationships (GRs) form an important level of natural language processing, but different sets of GRs are useful for different purposes. Therefore, one may often only have time to obtain a small training corpus with the desired GR annotations. To boost the performance from using such a small training corpus on a transformation rule learner, we use existing systems that find related types of annotations.