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Stemming is the process of reducing words to their root or base form. The purpose of stemming is to remove suffixes from words to create a uniform representation of the word for natural language processing tasks such as information retrieval, search engines, and text analysis.
The most commonly used stemming algorithm is the Porter Stemmer, developed by Martin Porter in 1980. The Porter Stemmer applies a set of rules to iteratively remove suffixes from words, making it highly efficient and widely used in search engines and NLP applications (Porter, 1980).
Another well-known algorithm is the Lancaster Stemmer, also called the Paice-Husk Stemmer, developed by Chris Paice and Gareth Husk. The Lancaster Stemmer is known for being more aggressive in reducing words to their base forms compared to the Porter Stemmer. It can create more drastic changes, which sometimes leads to overstemming ( Lancaster, 1990).
Although stemming is a useful technique, it can sometimes lead to issues such as overstemming (where too much of the word is removed) or understemming (where not enough of the word is removed). This is where more modern techniques like lemmatization offer a more precise approach to word normalization.