Stemmerit

Stemmerit is an open-source framework and evaluation suite for stemmer algorithms used in natural language processing. It provides a standardized set of benchmarks and metrics to assess how well different stemming approaches reduce words to their base forms while preserving linguistic utility across languages. The project aims to enable reproducible comparisons among rule-based, statistical, and neural stemmers, and to support multilingual evaluation.

Core metrics include precision and recall of stem assignment, over-stemming and under-stemming rates, boundary accuracy, and a stem-formation fidelity score that accounts for morphological relations. Additional measures assess the impact of stemming on downstream tasks such as information retrieval, clustering, or search ranking. Stemmerit includes language-specific gold-stem corpora, adapters for common stemmer implementations, and a harness for running experiments with transparent configuration and reporting.

Architecture comprises data layer, stemmer adapters, evaluation engine, and reporting module. It emphasizes reproducibility by recording dataset versions, parameter settings, and random seeds. The project supports integration with popular NLP tools and can export results in machine-readable formats for meta-analyses.

Applications span academic research, IR evaluation, and industrial preprocessing pipelines. By providing comparable baselines and transparent methodology, stemmerit helps developers select suitable stemming approaches and identify weaknesses tied to particular languages or domains.

Limitations include dependence on the quality of gold-standard stems, language coverage gaps, and the fact that stemming quality does not always correlate with end-task performance. Ongoing work focuses on expanding language support, optimizing evaluation speed, and integrating with lemmatization and morphological analysis pipelines.