Memory-Based Shallow Parsing

TL;DR

This paper explores memory-based learning methods for shallow parsing tasks, demonstrating effectiveness in base phrase identification but highlighting challenges in recognizing embedded structures.

Contribution

Introduces memory-based approaches with feature selection and system combination techniques for shallow parsing, evaluated on standard datasets.

Findings

Effective for base phrase identification

Less effective for embedded structure recognition

Outperforms some existing systems in specific tasks

Abstract

We present memory-based learning approaches to shallow parsing and apply these to five tasks: base noun phrase identification, arbitrary base phrase recognition, clause detection, noun phrase parsing and full parsing. We use feature selection techniques and system combination methods for improving the performance of the memory-based learner. Our approach is evaluated on standard data sets and the results are compared with that of other systems. This reveals that our approach works well for base phrase identification while its application towards recognizing embedded structures leaves some room for improvement.