A Task-Oriented Evaluation Framework for Text Normalization in Modern NLP Pipelines

TL;DR

This paper introduces a comprehensive, task-oriented framework for evaluating text stemming methods in NLP, considering utility, downstream impact, and semantic preservation, demonstrated through comparison of Bangla and English stemmers.

Contribution

It proposes a novel evaluation framework combining effectiveness, downstream task impact, and semantic similarity, addressing limitations of existing methods.

Findings

Bangla stemmer has high effectiveness but risks over-stemming.

English stemmer balances effectiveness and semantic safety.

The framework helps identify reliable stemming methods for NLP tasks.

Abstract

Text normalization is an essential preprocessing step in many natural language processing (NLP) tasks, and stemming is one such normalization technique that reduces words to their base or root form. However, evaluating stemming methods is challenging because current evaluation approaches are limited and do not capture the potential harm caused by excessive stemming; therefore, it is essential to develop new approaches to evaluate stemming methods. To address this issue, this study propose a novel, task-oriented approach to evaluate stemming methods, which considers three aspects: (1) the utility of stemming using Stemming Effectiveness Score (SES), (2) the impact of stemming on downstream tasks using Model Performance Delta (MPD), and (3) the semantic similarity between stemmed and original words using Average Normalized Levenshtein Distance (ANLD), thus providing a comprehensive evaluation framework. We apply our evaluation framework to compare two stemmers for Bangla (BNLTK) and English (Snowball), and our results reveal a significant issue, prompting us to analyze their performance in detail. While the Bangla stemmer achieves the highest SES (1.67) due to effective word reduction (CR = 1.90), SES alone is insufficient because our proposed safety measure, ANLD, reveals that this high SES is due to harmful over-stemming (ANLD = 0.26), which correlates with the observed decrease in downstream performance.In contrast, the English stemmer achieves a moderate SES (1.31) with a safe meaning distance (ANLD = 0.14), allowing its word reduction to contribute positively to downstream performance; therefore, it is a more reliable stemmer. Our study provides a valuable tool for distinguishing between potential efficiency gains (high SES) and meaning preservation (low ANLD).