Lost in Text. A Cross-Genre Analysis of Linguistic Phenomena within Text
Authors: Chiara Buongiovanni, Francesco Gracci, Dominique Brunato, Felice Dell'Orletta (Università di Pisa, CNR-ILC, 2019)
Topic: Textual Genres & Literature Linguistics

Abstract
Moving from the assumption that formal, rather than content features, can be used to detect differences and similarities among textual genres and registers, this paper presents a new approach to the linguistic profiling methodology, which focuses on the internal parts of a text. A case study is presented showing that it is possible to model the degree of variance within texts representative of four traditional genres and two levels of complexity for each.

Introduction
The combined use of corpus-based and computational linguistics methods to investigate language variation has become an established line of research. The heart of this research is the so-called ‘linguistic profiling’, a technique in which a large number of counts of linguistic features automatically extracted from parsed corpora are used as a text profile and can then be compared to average profiles for groups of texts (van Halteren, 2004). Although it was originally developed for authorship verification and recognition, linguistic profiling has been successfully applied to the study of genre and register variation, following Biber’s claim that 'linguistic features from all levels function together as underlying dimensions of variation, with each dimension defining a different set of linguistic relations among registers' (Biber, 1993). By modeling the ‘form’ of a text through large sets of linguistic features extracted from representative corpora, it has been possible not only to enhance automatic classification of genres (Stamatatos et al., 2001), but also to gain a better understanding of the impact of features in classifying genres and text varieties (Cimino et al., 2017). This paper moves in this framework but presents a new approach to linguistic profiling, in which the unit of analysis is not the document as a whole entity, but the internal parts in which it is articulated. In this respect, our perspective is similar to the one proposed by Crossley et al. (2011), who developed a supervised classification method based on linguistically motivated features to discriminate paragraphs with a specific rhetorical purpose within English students’ essays. However, differently from that work, we focus on Italian and enlarge the analysis to four traditional textual genres and two levels of language complexity for each. The aim is i) to explore to what extent the internal structure of a text can be modeled via linguistic features automatically extracted from texts, and ii) to study whether the variance across different parts of a text changes according to genre and level of complexity within genre.

Conclusion
In this paper we have presented a novel approach to the study of language variation, which relies on the prerequisites of the linguistic profiling methodology but with the specific purpose of modeling the stylistic form of the different parts within a text. A cross-genre investigation on four traditional genres in Italian, and two levels of complexity for each, showed that morpho-syntactic and syntactic features are differently distributed across subsections of texts belonging to a specific genre and language variety. This approach has important implications for research on genre variation since it suggests that the characterization of texts and texts varieties should benefit by inspecting corpora from this fine-grained perspective. A better understanding of linguistic phenomena characterizing the introductory, middle and conclusive parts of a text is also highly relevant not only to enhance automatic genre classification but also for other natural language processing applications devoted to modeling style: e.g. in education, as a component of intelligent tutoring systems able to provide detailed feedback to students in writing courses or for the automatic generation of texts with the stylistic properties of a specific genre and level of complexity.