Segmentation and Clustering of Textual Sequences: a Typological Approach

The long term goal of this research is to develop a program able to produce an automatic segmentation and categorization of textual sequences into discourse types. In this preliminary contribution, we present the construction of an algorithm which takes a segmented text as input and attempts to produce a categorization of sequences, such as narrative, argumentative, descriptive and so on. Also, this work aims at investigating a possible convergence between the typological approach developed in particular in the field of text and discourse analysis in French by Adam (2008) and Bronckart (1997) and unsupervised statistical learning.

A Typology of Identity-Related Crime

Identification is ever more important in the online world, and identity-related crime is a growing problem related to this. This new category of crime is not restricted to high-profile instances of identity 'theft' or identity fraud; it is wide-ranging and complex, ranging from identity deletion to unlawful identity creation and identity 'theft'. Commonly accepted definitions are lacking, thus blurring available statistics, and policies to combat this new crime are piecemeal at best. To assess the real nature and magnitude of identity-related crime, and to be able to discuss how it can be combated, identity-related crime should be understood in all its aspects. As a first key step, this article introduces a typology of identity-related crime, consisting of conceptual, technical and legal categories, that can be used as a comprehensive framework for future research, countermeasures and policies related to identity related crime.

Variants : The Journal of the European Society for Textual Scholarship

This volume is the 10th issue of Variants . In keeping with the mission of the European Society for Textual Scholarship, the articles are richly interdisciplinary and transnational. They bring to bear a wide range of topics and disciplines on the field of textual scholarship: historical linguistics, digital scholarly editing, classical philology, Dutch, English, Finnish and Swedish Literature, publishing traditions in Japan, book history, cultural history and folklore. The questions that are explored - what texts are worth editing? what is the nature of the relationship between text, work, document and book? what is a critical digital edition? - all return to fundamental issues that have been at the heart of the editorial discipline for decades. With refreshing insight they assess the increasingly hybrid nature of the theoretical considerations and practical methodologies employed by textual scholars, while reasserting the relevance and need for producing scholarly editions, whether in print or digital, and continuing advanced research in bibliographical codes, textual transmissions, genetic dossiers, the fluidity of texts and other such subjects that connect textual scholarship with broader investigations into our nations' literary culture and written heritage.

Textual autocorrelation : formalism and illustrations

Abstract Textual autocorrelation is a broad and pervasive concept, referring to the similarity between nearby textual units: lexical repetitions along consecutive sentences, semantic association between neighbouring lexemes, persistence of discourse types (narrative, descriptive, dialogal...) and so on. Textual autocorrelation can also be negative, as illustrated by alternating phonological or morpho-syntactic categories, or the succession of word lengths. This contribution proposes a general Markov formalism for textual navigation, and inspired by spatial statistics. The formalism can express well-known constructs in textual data analysis, such as term-document matrices, references and hyperlinks navigation, (web) information retrieval, and in particular textual autocorrelation, as measured by Moran's I relatively to the exchange matrix associated to neighbourhoods of various possible types. Four case studies (word lengths alternation, lexical repulsion, parts of speech autocorrelation, and semantic autocorrelation) illustrate the theory. In particular, one observes a short-range repulsion between nouns together with a short-range attraction between verbs, both at the lexical and semantic levels. Résumé: Le concept d'autocorrélation textuelle, fort vaste, réfère à la similarité entre unités textuelles voisines: répétitions lexicales entre phrases successives, association sémantique entre lexèmes voisins, persistance du type de discours (narratif, descriptif, dialogal...) et ainsi de suite. L'autocorrélation textuelle peut être également négative, comme l'illustrent l'alternance entre les catégories phonologiques ou morpho-syntaxiques, ou la succession des longueurs de mots. Cette contribution propose un formalisme markovien général pour la navigation textuelle, inspiré par la statistique spatiale. Le formalisme est capable d'exprimer des constructions bien connues en analyse des données textuelles, telles que les matrices termes-documents, les références et la navigation par hyperliens, la recherche documentaire sur internet, et, en particulier, l'autocorélation textuelle, telle que mesurée par le I de Moran relatif à une matrice d'échange associée à des voisinages de différents types possibles. Quatre cas d'étude illustrent la théorie: alternance des longueurs de mots, répulsion lexicale, autocorrélation des catégories morpho-syntaxiques et autocorrélation sémantique. On observe en particulier une répulsion à courte portée entre les noms, ainsi qu'une attraction à courte portée entre les verbes, tant au niveau lexical que sémantique.