An algorithm for suffix stripping

By M. Porter. 1997

Subfields: Information Retrieval & Search, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Retrieval & Search

In Natural Language Processing

Citations in the corpus

Few-Shot Text Generation with Natural Language Instructions (2020, methodology): “For evaluation, we follow Zhang et al. (2020) and report Rouge1, Rouge2 and RougeL (R1/R2/RL) F1 scores (Lin, 2004) after stemming using the Porter algorithm (Porter, 1997).”
Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics (2013, methodology): “Following the work of Lin (2004), we use a stemmer (Porter, 1980) and remove stopwords before compute Rouge scores.”
Natural Language Processing (Almost) from Scratch (2011, methodology): “We also tried suffixes obtained with the Porter (1980) stemmer and obtained the same performance as when using two character suffixes.”
Learning Dependency-Based Compositional Semantics (2011, methodology): “Finally, to determine triggering, we stem all words using the Porter stemmer (Porter 1980), so that mountains triggers the same predicates as mountain.”
From Frequency to Meaning: Vector Space Models of Semantics (2010, methodology): “In English, affixes are simpler and more regular than in many other languages, and stemming algorithms based on heuristics (rules of thumb) work relatively well (Lovins, 1968; Porter, 1980; Minnen, Carroll, & Pearce, 2001).”
Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics (2004, methodology): “3. Stem set: All words were converted into lower case and stemmed using the Porter stemmer ( Porter 1980 ).”
Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics (2003, methodology): “Stopwords are ignored during the computation of Ngram(1,4) scores and words are stemmed using a Porter stemmer (Porter 1980).”
Decision support from financial disclosures with deep neural networks and transfer learning (2017, neutral): ““increased” and “increasing” are both mapped onto “increas” [39].”
FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, methodology): “We employ the Porter Stemming algorithm for term normalisation to make our matching process more robust [57].”
UNAL-NLP: Combining Soft Cardinality Features for Semantic Textual Similarity, Relatedness and Entailment (2014, methodology): “Then each word was reduced to its stem using the Porter’s algorithm (Porter, 1980) and a idf weight (Jones, 2004) was associated to each stem (wai weights in eq.”
The Edinburgh/JHU Phrase-based Machine Translation Systems for WMT 2015 (2015, background): “clusters (50 classes), morphological tags, partof-speech tags, and word stems on the German side (Schmid, 2000), as well as Och clusters (50 classes), part-of-speech tags (Ratnaparkhi, 1996), and word stems (Porter, 1980) on the English”
Adaptation of machine translation for multilingual information retrieval in the medical domain (2014, methodology): “We use the Porter’s Snowball stemmer for the source side languages (Czech, German, French) and the original Porter’s stemmer for English [93].”
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).”
Structure-Augmented Keyphrase Generation (2021, methodology): “For all datasets, following (Meng et al., 2017), we applied stemming using Porter Stemmer (Porter, 1980)9 as preprocessing, where duplicate keyphrases were removed after stemming.”
The Pyramid Match Kernel: Efficient Learning with Sets of Features (2007, methodology): “Suffixes were removed from words using the Porter stemmer (Porter, 1980), and the WordNet set of stop-list words were removed.”
Meteor-s web service annotation framework (2004, methodology): “The TokenMatcher uses the Porter Stemmer [17] algorithm, tokenization, stop-words removal, and substring matching techniques to find the similarity.”
Overview of Touché 2022: Argument Retrieval (2022, unknown): “All texts were processed with the Porter stemmer [61], removing stopwords using different lists such as Snowball [62], a default Lucene stopword list, a custom list containing the 400 most frequent terms in the retrieval collection excluding the comparison objects contained in the 50 search topics,…”