What About Sequential Data Mining Techniques to Identify Linguistic Patterns for Stylistics?

Edited by Alexander Gelbukh. - 1st ed. 2012. Édition papier : ISBN 9783642286032. Édition numérique: ISBN 978-3-642-28604-9. DOI : 10.1007/978-3-642-28604-9. This two-volume set, consisting of LNCS 7181 and LNCS 7182, constitutes the thoroughly refereed proceedings of the 13th International Conference on Computer Linguistics and Intelligent Processing, held in New Delhi, India, in March 2012. ; International audience ; In this paper, we study the use of data mining techniques for stylistic analysis, from a linguistic point of view, by considering emerging sequential patterns. First, we show that mining sequential patterns of words with gap constraints gives new relevant linguistic patterns with respect to patterns built on n-grams. Then, we investigate how sequential patterns of itemsets can provide more generic linguistic patterns. We validate our approach from a linguistic point of view by conducting experiments on three corpora of various types of French texts (Poetry, Letters, and Fictions). By considering more particularly poetic texts, we show that characteristic linguistic patterns can be identified using data mining techniques. We also discuss how to improve our proposed approach so that it can be used more efficiently for linguistic analyses.