Upcoming talk by Eugenio Petrovic @DS² 2021 conference

Posted on February 15, 2021 by DR2 Turin Group

We are glad to announce an upcoming talk by Eugenio Petrovich at the DS² 2021 conference (Digital Studies of Digital Science) @UCLouvain. Here more details on the program.

The conference aims to connect two groups of scholars working on separate trends: the digitalizing of science’s products on one side, and the spread of digital methods on the other side (network and citation analysis, textual analysis and other tools of the digital humanities). The conference will take place, virtually, from March 15–18, 2021. Among the speakers, keynote talks will be held by Katy Börner, Sabina Leonelli, and Christophe Malaterre.

Eugenio Petrovich will present his work on Acknowledgments Co-Mention Networks: A new method for mapping the social structure of scholarly fields.

An abstract of his speech is reported below:

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Call for tender: postdoctoral position – Quality open data production. Deadline 25/02/2021

Posted on February 8, 2021 by DR2 Turin Group

The Institute for Cognition Sciences and Technologies (CNR) opens the selection for one postdoctoral position (informatics/engineering) in graphs design and development for management and representation of heterogeneously sourced data in sanitary and environmental domains, under the “WHOW – Water Health Open knoWledge” research project.

The selection is open to both Italian and foreign candidates.

Call for tender and submission form (ita/eng) are downloadable here.

 

 

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Three postdoctoral positions at UvA- Research Priority Area Humane AI

Posted on January 29, 2021 by DR2 Turin Group

The University of Amsterdam’s interdisciplinary Research Priority Area Humane AI is looking for 3 postdoctoral researchers in the areas of law, social sciences and humanities. The overarching goal of these positions is to develop insights and principles for responsible and human-centric AI from a Social Science-Humanities (SSH) perspective: How can SSH research contribute to responsible AI, e.g. in sectors like media, culture/language or justice; what are the contextual socio-economic circumstances that need to be fulfilled for really responsible AI?; what is irresponsible AI? What is the role of human behaviour, law and ethics in creating the framework conditions for responsible AI?

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Uncovering the social network of recent analytic philosophy – A talk by Eugenio Petrovich

Posted on January 25, 2021 by DR2 Turin Group

We are glad to share the video of the talk given by DR2 member Eugenio Petrovich in the 4th Annual TiLPS History of Analytic Philosophy Workshop @ TiLPS (Tilburg Center for Moral Philosophy, Epistemology and Philosophy of Science).

The talk presents Petrovich’s research (still in progress) on the acknowledgements found in the top-five analytic philosophy journals. Acknowledgements are shown to be a fruitful source of information to reconstruct the social context of analytic philosophy, alongside a network of informal collaborations.

In the video, you will find all the details of the research:

 

 

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On-line workshop, February 17-19 – Sentiment analysis on multilingual 18th-century corpora

Posted on January 11, 2021 by DR2 Turin Group

We give notice of the on-line workshop Sentiment Analysis in Literary studies organized by the Centre for Information Modelling of the University of Graz.

Sentiment analysis is a common task in literary studies, yet sitting outside the mainstream of analytic computational procedures applied to philosophical corpora. Critic facets of sentiment analysis procedures for historical-philosophical analysis lie primarily on tools’ dictionary-dependancy, from which follow difficulties in obtaining in-depth historical understanding and the possibility of arbitrary biases in interpretation of both the retrieved sentiment and its object. However, reasons for such a sidelining hold when they are referred to techniques and workflows commonly deployed and followed in order to achieve sentiment analysis, while wanting to perform such a task might not be a radically flawed endeavour per se, as long as researchers set a well-grounded research framework.

The workshop’s programme anticipates a thorough examination of existing tools, approaches and workflows as well as of preliminary steps such as textual preprocessing, and it is uncommonly devoted to the analysis of 18th century texts.

The workshop fills in the context of the project “DiSpecs – Distant Spectators. Distant Reading for Periodicals of the Enlightenment”, funded by CLARIAH-AT and in cooperation with the Institute for Interactive Systems and Data Science, the Know-Center GmbH Graz and the Centre for Information Modelling – Austrian Centre for Digital Humanities (ZIM-ACDH) and the Institute for Romance Studies. The project aims to investigate the digitized, TEI encoded and semantically enriched texts of The Spectators (http://gams.uni-graz.at/mws), with quantitative methods of data analysis referred to as distant reading and macroanalysis (topic modeling, stylometry, meme diffusion, sentiment analysis and community detection).
The 18th-century journalistic genre of “spectators” (or moralistic sheets) had a large audience of urban readers and played an essential role in public opinion genesis. This project endeavours to create an integral database for all the moralistic press in French, Italian, Spanish, English, German, and Portuguese. In this context, the Spectator discourses’ quantitative analysis aims to enhance and improve micro-narration studies regarding the repetition of motifs throughout different journals.

Official information is reported below.

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Happy new year!

Posted on January 2, 2021 by DR2 Turin Group

DR2 team wishes everybody a happy new year!

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PostDoc Fellowship Opportunity in Ontology for Industry Laboratory for Applied Ontology (LOA), ISTC-CNR at Trento (Italy)

Posted on December 24, 2020 by DR2 Turin Group

We are pleased to forward information about a PostDoc Fellowship in Ontology in Trento.

Research will focus on the study of ontology for industry with the possibility to engage in both foundational ontology and the development of a domain ontology for a real industrial case. The research topics will be fixed depending on your interests. The selection will be based on your academic titles and research record.

Application deadline: Jan. 13, 2021 (see below)

Job Description

Your research will focus on the use of applied ontology as a driver for data and information interoperability, following the FAIR data principles, in the industrial domain and in particular in areas like aerospace, manufacturing and material engineering. You will work on three types of activities (emphasis depends on your own interests): (1) study and formalization of the relationships across existing top-level and middle-level ontologies including the identification of commonalities and formal alignments among them; (2) collaborate in the development and implementation of an ecosystem of ontologies and knowledge bases for automatic or semi-automatic data exchange (preserving semantic interpretation); (3) collaborate on the topics of the European project “OntoCommons”.

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Analytic and Continental Philosophy: playing around with quantitative methods

Posted on December 14, 2020 by DR2 Turin Group

by Pietro Lana

 

What can quantitative methods tell us about the differences between the Analytic and the Continental philosophical traditions? Attempts to define, characterize and distinguish the two have led to such a variety of positions that even the most cautious proposals have been put into question (Glock, 2008). The difficulties in identifying sufficient or necessary differentiation criteria – be they doctrinal, methodological, stylistic or thematic – seem to call for a different approach. It was 1993 when Michael Dummet in his Origins of Analytical Philosophy compared the analytic and the continental traditions to the Rhine and the Danube: rivers that after rising close to one another flow into different seas. What if, instead of attempting to provide a cartography of the two rivers, were we to dive into their waters? What follows is a brief exploration of some possible applications of quantitative methods to the study of the differences between the two traditions. As any exploration, it is not meant to provide conclusive results on the subject, but rather to shed light on further possible routes. 

The assessed corpus consists of all the articles published between 1980 and 2018 by four Anglophone philosophy journals, two of them belonging to the analytic tradition (“Philosophical Studies”, “Mind”) and two of them belonging to the continental tradition (“Continental Philosophy Review”, “Research in Phenomenology”). Because of the comparative nature of the study, the journals were chosen both on the basis of their relevance – in terms of average number of weighted citations – and on the basis of their representativeness: all four journals explicitly present themselves as being part of one of the two traditions. In conducting the textual analysis, the “analytic” and “continental” corpora have also been divided into further subcorpora by decades of publication, in order to allow for the results to show eventual changes over time.

The text mining software employed to assess the corpus is Lancsbox, developed at Lancaster University by Vaclav Brezina, Matthew Timperley and Anthony McEnery. It has been chosen because it allows for a variety of analysis on the language data that are present in a given corpus, such as type/token ratio, frequency, dispersion, keyword generation and collocation. The collocation graphs below have been generated by using Gephi, a network exploration software developed by Mathieu Bastian and Eduardo Ramos Ibañez. 

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Posted in Data-Driven Research, Digital Humanities, Distant Reading, History of analytic philosophy, History of philosophy, Quantitative methods, Text mining, Text-Mining | 1 Comment

New DR2 member

Posted on December 4, 2020 by DR2 Turin Group

We warmly welcome Pietro Lana, master student at the philosophy department here in Turin, as a new member of the DR2 group. Check him and other DR2 affiliates in our People section.

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Considerations about corpus-dependency of topic modelling with Mallet

Posted on November 30, 2020 by DR2 Turin Group

By Sara Garzone and Nicola Ruschena

In the context of text mining, topic modelling analyses co-occurrence patterns among textual data, in order to isolate clusters from the set of expressions occurring in a corpus. Topic modelling aims at extracting topics occurring in a corpus and categorize documents on the basis of their semantic content. It often represents an appealing approach for data-driven analysis in short-run projects, for it is an unsupervised method, i.e., there is no requirement for algorithm training from labelled data, whose production is quite a demanding task. Moreover, software programs that are executable from command line or user interface have been developed to perform topic modelling, so as to provide more friendly environments for researchers who are not much acquainted with code design.

Mallet is a tool for topic modelling: it is a Java-based package for statistical natural language processing, which was initially developed by Andrew McCallum at the University of Massachusetts. It allows topic modelling on textual corpora, without requiring advanced technical knowledge in statistics and programming. 

Mallet’s topic modelling is based on the Latent Dirichlet Allocation (LDA) model, a Bayesian probabilistic generative model which has been applied for the first time to text classification tasks by David Blei et al. in 2003, and thereafter has become the standard for probabilistic text categorization under latent semantic hypotheses. Along with many other techniques in the field of natural language processing, topic modelling relies upon the so-called distributional hypothesis (Harris 1954), according to which words occurring in the same contexts tend to have similar meanings. 

From co-occurrence analysis and clustering it is then possible to expect clusters to reflect semantic proximity relations, or topics. With advanced applications of probabilistic models, a categorization of documents can then be obtained on the basis of the degree of probability of their being a member of detected topics. The underlying assumption is that in each document a probabilistic distribution of every topic can be recognized. With LDA-based topic modelling one can try to understand which of the topics that have been detected in the corpus are likely to be present in each document, given the occurring terms.
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