3,596 research outputs found
Drawing Elena Ferrante's Profile. Workshop Proceedings, Padova, 7 September 2017
Elena Ferrante is an internationally acclaimed Italian novelist whose real identity has been kept secret by E/O publishing house for more than 25 years. Owing to her popularity, major Italian and foreign newspapers have long tried to discover her real identity. However, only a few attempts have been made to foster a scientific debate on her work.
In 2016, Arjuna Tuzzi and Michele Cortelazzo led an Italian research team that conducted a preliminary study and collected a well-founded, large corpus of Italian novels comprising 150 works published in the last 30 years by 40 different authors. Moreover, they shared their data with a select group of international experts on authorship attribution, profiling, and analysis of textual data: Maciej Eder and Jan Rybicki (Poland), Patrick Juola (United States), Vittorio Loreto and his research team, Margherita Lalli and Francesca Tria (Italy), George Mikros (Greece), Pierre Ratinaud (France), and Jacques Savoy (Switzerland).
The chapters of this volume report the results of this endeavour that were first presented during the international workshop Drawing Elena Ferrante's Profile in Padua on 7 September 2017 as part of the 3rd IQLA-GIAT Summer School in Quantitative Analysis of Textual Data. The fascinating research findings suggest that Elena Ferrante\u2019s work definitely deserves \u201cmany hands\u201d as well as an extensive effort to understand her distinct writing style and the reasons for her worldwide success
A complex network approach to stylometry
Statistical methods have been widely employed to study the fundamental
properties of language. In recent years, methods from complex and dynamical
systems proved useful to create several language models. Despite the large
amount of studies devoted to represent texts with physical models, only a
limited number of studies have shown how the properties of the underlying
physical systems can be employed to improve the performance of natural language
processing tasks. In this paper, I address this problem by devising complex
networks methods that are able to improve the performance of current
statistical methods. Using a fuzzy classification strategy, I show that the
topological properties extracted from texts complement the traditional textual
description. In several cases, the performance obtained with hybrid approaches
outperformed the results obtained when only traditional or networked methods
were used. Because the proposed model is generic, the framework devised here
could be straightforwardly used to study similar textual applications where the
topology plays a pivotal role in the description of the interacting agents.Comment: PLoS ONE, 2015 (to appear
Sociolinguistic Features for Author Gender Identification: From Qualitative Evidence to Quantitative Analysis
This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Quantitative Linguistics on 7 October 2016, available online: http://www.tandfonline.com/10.1080/09296174.2016.1226430. The Accepted Manuscript is under embargo. Embargo end date: 7 April 2018.Theoretical and empirical studies prove the strong relationship between social factors and the individual linguistic attitudes. Different social categories, such as gender, age, education, profession and social status, are strongly related with the linguistic diversity of peopleâs everyday spoken and written interaction. In this paper, sociolinguistic studies addressed to gender differentiation are overviewed in order to identify how various linguistic characteristics differ between women and men. Thereafter, it is examined if and how these qualitative features can become quantitative metrics for the task of gender identification from texts on web blogs. The evaluation results showed that the âsyntactic complexityâ, the âtag questionsâ, the âperiod lengthâ, the âadjectivesâ and the âvocabulary richnessâ characteristics seem to be significantly distinctive with respect to the authorâs gender.Peer reviewedFinal Accepted Versio
Probing the topological properties of complex networks modeling short written texts
In recent years, graph theory has been widely employed to probe several
language properties. More specifically, the so-called word adjacency model has
been proven useful for tackling several practical problems, especially those
relying on textual stylistic analysis. The most common approach to treat texts
as networks has simply considered either large pieces of texts or entire books.
This approach has certainly worked well -- many informative discoveries have
been made this way -- but it raises an uncomfortable question: could there be
important topological patterns in small pieces of texts? To address this
problem, the topological properties of subtexts sampled from entire books was
probed. Statistical analyzes performed on a dataset comprising 50 novels
revealed that most of the traditional topological measurements are stable for
short subtexts. When the performance of the authorship recognition task was
analyzed, it was found that a proper sampling yields a discriminability similar
to the one found with full texts. Surprisingly, the support vector machine
classification based on the characterization of short texts outperformed the
one performed with entire books. These findings suggest that a local
topological analysis of large documents might improve its global
characterization. Most importantly, it was verified, as a proof of principle,
that short texts can be analyzed with the methods and concepts of complex
networks. As a consequence, the techniques described here can be extended in a
straightforward fashion to analyze texts as time-varying complex networks
A Data-Oriented Model of Literary Language
We consider the task of predicting how literary a text is, with a gold
standard from human ratings. Aside from a standard bigram baseline, we apply
rich syntactic tree fragments, mined from the training set, and a series of
hand-picked features. Our model is the first to distinguish degrees of highly
and less literary novels using a variety of lexical and syntactic features, and
explains 76.0 % of the variation in literary ratings.Comment: To be published in EACL 2017, 11 page
Automatic IQ estimation using stylometry methods.
Stylometry is a study of text linguistic properties that brings together various field of research such as statistics, linguistics, computer science and more. Stylometry methods have been used for historic investigation, as forensic evidence and educational tool. This thesis presents a method to automatically estimate individualâs IQ based on quality of writing and discusses challenges associated with it. The method utilizes various text features and NLP techniques to calculate metrics which are used to estimate individualâs IQ. The results show a high degree of correlation between expected and estimated IQs in cases when IQ is within the average range. Obtaining good estimation for IQs on the high and low ends of the spectrum proves to be more challenging and this work offers several reasons for that. Over the years stylometry benefitted from wide exposure and interest among researches, however it appears that there arenât many studies that focus on using stylometry methods to estimate individualâs intelligence. Perhaps this work presents the first in-depth attempt to do s
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