Arjuna Tuzzi

Analyzing written communication in AAC contexts: a statistical perspective.

This research note focuses on some of the opportunities provided by the statistical analysis of textual data, by illustrating examples of the use of lexicon-based quantitative measures with texts within a particular context of augmentative and alternative communication. The corpus is composed of 12 essays produced by six individuals with autism and six participants without disabilities in a control group during sessions of facilitated communication. The study raises questions that can be answered thanks to the statistical methods implemented in the text analysis framework and other procedures that may be used to identify the characteristics of texts (and their writers) and compare texts (or subcorpora). The aim is to discuss strengths, weaknesses, opportunities, and threats of the approach and to highlight its connections to qualitative approaches.

Zipf's Laws in Italian Texts

Abstract There are texts which do not conform to the classical Zipfs law, but even if it holds, some questions remain open. In order to test the validity of Zipfs law we analysed a corpus composed of the End of Year addresses delivered by ten presidents of the Italian Republic in the period 1949–2008. The results show that Zipfs law is an adequate model and that the corpus has a unique style, even if the texts were compiled by at least two persons. The analyses allow us to find a position for each president on the synthetism/analytism scale. Presidents Pertini and Scalfaro show the best well-defined individual characteristic features.

Improving Labbé’s Intertextual Distance: Testing a Revised Version on a Large Corpus of Italian Literature

Abstract Moving from Labbé’s proposal envisaging the use of intertextual distance to measure the similarity (and dissimilarity) of texts, this paper proposes a new calculation procedure based on repeated observations of intertextual distance between pairs of equal-sized text chunks. The implementation of this procedure on a large corpus including 160 Italian novels provides information on the values produced by measuring intertextual distance in (both lemmatized and non-lemmatized) literary texts written in Italian. In order to show the improvement achieved through this iterative procedure compared to the original version, distance values are assessed in terms of their ability to recognize the author as the factor responsible for text pairing.

Recent Contributions to Quantitative Linguistics

Quantitative Linguistics is a rapidly developing discipline covering more and more areas of linguistic and textological research. The book represents an overview of the state of the art in Quantitative Linguistics, its scope and reach. Some of the topics: linguistic laws, frequency analyses, synergetic models of language, networks, part-of-speech systems, authorship attribution, polyfunctionality and polysemy, and opinion target identification.

Statistical Analysis of Textual Data from Corpora of Written Communication – New Results from an Italian Interdisciplinary Research Program (EASIEST)

Autism spectrum disorder (ASD) is a form of pervasive developmental disorder characterized by complex communication needs and early onset. The triad of symptoms for diagnosing ASD includes three areas: (a) social interaction; (b) language and communication; (c) behavior, activities, and interests (American Psychiatric Association, 2000). Complex qualitative and quantitative language and communication needs are acknowledged among the specific characteristics of this disorder, though defining and identifying these needs often proves a difficult task (Boucher, 2003; Sikora, Hartley, Mccoy, Gerrard-Morris, & Dill, 2008; Snyder, Miller, & Stein, 2008). Enhancing effective communication in everyday life and investigating new ways to help individuals with ASD (IWA) to communicate are fundamental issues (Tager-Flusberg & Caronna, 2007; Ostryn, 2008; Koegel & Brown, 2007) and, more in general, recent results (Rapin & Tuchman, 2008) stress the growing need for special services and treatments for an increasing number of children (and adults).

Learning the evolution of disciplines from scientific literature: A functional clustering approach to normalized keyword count trajectories

Abstract The growing availability of large diachronic corpora of scientific literature offers the opportunity of reading the temporal evolution of concepts, methods and applications, i.e., the history of disciplines involved in the strand under investigation. After a retrieval process of the most relevant keywords, bag-of-words approaches produce wordsu2009u202f×u202fu2009time-points contingency tables, i.e. the frequencies of each word in the set of texts grouped by time-points. Through the analysis of word counts over the observed period of time, main purpose of the study is, after reconstructing the “life-cycle” of words, clustering words that have similar life-cycles and, thus, detecting prototypical or exemplary temporal patterns. Unveiling such relevant and (through expert opinion) meaningful inner dynamics enables us to trace a historical narrative of the discipline of interest. However, different history readings are possible depending on the type of data normalization, which is needed to account for the fluctuating size of texts across time and the general problems of data sparsity and strong asymmetry. This study proposes a methodology consisting of (1) a stepwise information retrieval procedure for keywords’ selection and (2) a functional clustering two-stage approach for statistical learning. Moreover, a sample of possible normalizations of word frequencies is considered, showing that the different concept of curve similarity induced in clustering by the type of transformation heavily affects groups’ composition and size. The corpus of titles of scientific papers published by the American Statistical Association journals in the time span 1888–2012 is examined for illustration.