RFreeStem: A multilanguage rule-free stemmer

Xavier Baril (1) , Oihana Coustié (1, 2) , Josiane Mothe (2, 3) , Olivier Teste (2, 3)

1 AIRBUS Operations Ltd.
2 IRIT-SIG - Systèmes d’Informations Généralisées
3 UT2J - Université Toulouse - Jean Jaurès

With the large expansion of available textual data, text mining has become of specialinterest. Due to their unstructured nature, such data require important preprocessing steps.Among them, stemming is a popularly used preprocessing method that extracts the root of thewords. However, the most popular algorithms are based on the application of rules, and there-fore highly language-related. We propose a new approach, the RFreeStem, that is rather basedon corpus and can therefore be applied on many languages.

Mots clés: Text mining; Information retrieval; Sentiment analysis; Stemmer; NLP

Domaines: Informatique et langage [cs.CL]; Base de données [cs.DB]

Xavier Baril, Oihana Coustié, Josiane Mothe, Olivier Teste. RFreeStem: A multilanguage rule-free stemmer. 37e Congres Informatique des Organisations et Systemes d'Information et de Decision (INFORSID 2019), Jun 2019, Paris, France. pp.12-29. ⟨hal-02891675⟩