Stemming

Stemming is a text normalization technique used in natural language processing and information retrieval to reduce words to their base or stem form by removing affixes. The goal is to group together related word variants so they can be matched or indexed together, even when they appear in different forms. Stemmers do not attempt to produce linguistically correct lemmas, and the resulting stem may not be a valid word in the language.

Stemming algorithms can be rule-based or statistical. Early rule-based systems include the Lovins stemmer and the Porter stemmer, both of which apply sequences of affix-stripping rules. Snowball is a newer, language-aware framework that provides language-specific stemmers. Some approaches are designed to be conservative (light stemmers) to reduce over-stemming, while others are more aggressive.

Stemming versus lemmatization: stemming reduces words to a stem that may not be a dictionary form, whereas lemmatization maps words to their dictionary lemmas using linguistic analysis. Stemming is typically faster and simpler to implement, making it well suited for large-scale indexing and search. Lemmatization tends to produce more accurate and readable bases but requires lexical resources and more computation.

Applications and limitations: In information retrieval, stemming can increase recall by treating related forms as equivalent, but it may reduce precision if different words are incorrectly conflated. Stemming is language-dependent and can struggle with irregular forms or morphologically rich languages. In multilingual or mixed-language texts, language detection is often needed before stemming.

Overall, stemming remains a foundational preprocessing step in many IR and NLP pipelines, balancing simplicity and effectiveness for reducing lexical variation.