The Porter stemming algorithm: then and now

By Peter Willett. Program, 2006

448 citations recorded by the source corpus.

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NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction (2020, methodology): “To reduce the annotation efforts and ensure the popularity of rules, we convert all words to their root form using Porter stemming algorithm [33] and only keep the rules whose stemmed form appears at least N times in the whole corpus.”
Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code (2020, background): “Stemming [89] can reduce vocabulary size, but loses information: it is not always obvious how to recover the original word from its stem.”
Machine Learning-Based Sentiment Analysis for Twitter Accounts (2018, methodology): “For the purpose of smoothing the data by removing zero values, it used Laplace and Porter stemmer [17,18].”
Modeling Vocabulary for Big Code Machine Learning (2019, methodology): “We considered stemming [73] to reduce vocabulary size but decided against since: 1) stemming approaches work in the context of a specific language, while the corpus is multi-lingual to some degree; 2) stemming loses information: it is not always obvious how to recover the original word from its…”
Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook (2020, methodology): “Following this, token n-grams were constructed consisting of one to three words stemmed using Porter’s stemming algorithm [17].”