A Learning Approach to Shallow Parsing

TL;DR

This paper introduces a SNoW-based learning method for shallow parsing, demonstrating its effectiveness in identifying syntactic patterns like Noun-Phrases and Subject-Verb phrases, and compares modeling approaches for pattern recognition.

Contribution

It presents a novel learning approach for shallow parsing using simple predictors, with experimental validation showing superior results and insights into modeling strategies.

Findings

The approach achieves competitive results on NP and SV phrase detection.

Open/close predictors outperform inside/outside predictors for pattern learning.

Experimental results compare favorably with the best published results.

Abstract

A SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic patterns by combining simple predictors to produce a coherent inference. Two instantiations of this approach are studied and experimental results for Noun-Phrases (NP) and Subject-Verb (SV) phrases that compare favorably with the best published results are presented. In doing that, we compare two ways of modeling the problem of learning to recognize patterns and suggest that shallow parsing patterns are better learned using open/close predictors than using inside/outside predictors.