Snowball: A language for stemming algorithms

By M. Porter. 2001

793 citations recorded by the source corpus.

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Citations in the corpus

Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network (2018, methodology): “Finally, the following steps were implemented: Stop word removal: all stop words such as ‘‘and’’, ‘‘or’’, ‘‘by’’, etc., were removed based on the English stop words dictionary proposed by Porter and Martin [26].”
Representing General Relational Knowledge in ConceptNet 5 (2012, methodology): “, 2004), and in many European languages we use the Snowball stemmer for that language (Porter, 2001) to remove stop words and reduce inflected words to a common stem.”
Meteor Universal: Language Specific Translation Evaluation for Any Target Language (2014, methodology): “Stem: Stem words using a language appropriate Snowball Stemmer (Porter, 2001) and match if the stems are identical.”
Augmenting API Documentation with Insights from Stack Overflow (2016, methodology): “To ensure that the matching of sentences to patterns is not sensitive to different grammatical forms, we applied stemming to each word in the patterns and the sentences using Porter’s stemmer [27] before calculating the matches.”
The Meteor metric for automatic evaluation of machine translation (2009, methodology): “− Stem: Words are stemmed using the Snowball Stemmer Collection (Porter, 2001).”
Online Defect Prediction for Imbalanced Data (2015, methodology): “We use the snowBall stemmer [23] to group words of the same root and Weka [24] to obtain the bag-of-words features from both the commit messages and the source code.”
Document-level sentiment classification: An empirical comparison between SVM and ANN (2013, methodology): “Popular stemmer algorithms for the english language are Snowball (Porter, 2001), Porter (Porter, 1980) and Lovins (Lovins, 1968).”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, methodology): “Reference Name of the tool Purpose [251] TweetMotif Tokenization of tweets [204], [250] POS tagger Twitter POS tagger [244] TweetNLP(6) Twitter natural language processing [245] Lancaster stemming algorithm Stemmer [246] GNU Aspell Spell Checker [247] Snowball English stemmer [248] Stanford Log-line”
ConceptNet 3 : a Flexible , Multilingual Semantic Network for Common Sense Knowledge (2007, methodology): “We currently use Porter’s Snowball stemmer, in both its English(1) and Portuguese versions [11].”
A Comparative Study of Stemming Algorithms (2011, unknown): “Porters Stemmer Porters stemming algorithm [17, 18] is as of now one of the most popular stemming methods proposed in 1980.”
Computational approaches for mining user's opinions on the Web 2.0 (2014, methodology): “Stemming: Stemming is the process of reducing words to their base form; we used the Snowball stemmer (Porter, 2001) implemented in the Lucene.NET library.”
Preprocessing Techniques for Text Mining-An Overview Dr (2015, background): “The inflectional database reduces each surface word to the form which can be found in the dictionary, as follows [12]:”
Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach (2020, methodology): “For stemming task, we have utilized Snowball stemming algorithm [57].”
Radiology Objects in COntext (ROCO): A Multimodal Image Dataset (2018, methodology): “W ord Stemming: To reduce complexity, the captions are stemmed using Snowball Stemming [16].”
Meteor 1.3: Automatic Metric for Reliable Optimization and Evaluation of Machine Translation Systems (2011, methodology): “Stem: Stem words using a language-appropriate Snowball Stemmer (Porter, 2001) and match if the stems are identical.”
Character N-Gram Tokenization for European Language Text Retrieval (2004, methodology): “On occasion we have run experiments using one of the Snowball stemmers (Porter 2001), but this is not a regular”