1

Abstract: Similarities for textual data The evaluation of similarities between textual entities (documents, sentences, words...) is one of the central issues for the implementation of efficient methods for tasks such as description and exploration of textual data, information retrieval or knowledge extraction (text mining). The main purpose of this contribution is to propose a comparative presentation of different approaches used to define the notion of similarity in fields such as Textual Data Analysis, Information Retrieval or Text Mining. We first discuss some of the linguistic treatments (tagging, lemmatization, …) necessary for the pre-processing of the textual data and then analyze some of the measures (cosinus, chi-square, Kullback-Leibler) used to quantify the similarities (or dissimilarities) between textual entities. Finally, we present some techniques allowing to improve the quality of the similarities in the case where additional knowledge, such as external corpora or semantic graphs, is available