Stemming and lemmatization are text preprocessing techniques used to normalize words by reducing them to their root forms, but they differ significantly in approach and output. Stemming uses heuristic methods to strip affixes (prefixes or suffixes) from words, often resulting in non-standard root forms. For example, “running” and “runner” might both be reduced to “run,” while “studies” could become “studi.” This method is computationally inexpensive but may lead to inaccuracies or loss of meaning.
Lemmatization, on the other hand, takes a linguistics-based approach by transforming words into their canonical or dictionary form. It considers the word’s context and part of speech, ensuring grammatical correctness. For instance, “running” is lemmatized to “run,” and “better” becomes “good.” While lemmatization is more accurate and preserves semantic meaning, it is computationally more demanding than stemming.
The choice between stemming and lemmatization depends on the specific application. Stemming is suitable for tasks requiring high speed and lower precision, such as search engine indexing. Lemmatization, however, is ideal for applications like sentiment analysis or machine translation, where semantic accuracy is critical. Tools like NLTK and spaCy support both methods, allowing developers to customize preprocessing pipelines as needed.