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In den Computational Literary Studies (CLS) werden statistische Distinktivitätsmaße eingesetzt, um Features zu bestimmen, die charakteristisch für eine Textgruppe im Vergleich mit einer anderen Textgruppe sind. Allerdings erweisen sich die meisten vorhandenen Tools als ungeeignet, wenn Nutzer:innen ihre Analysen anpassen und eigene Parametereinstellungen vornehmen oder bestimmte Datenformate nutzen wollen. Um den Einsatz relevanter Maße für die kontrastive Textanalyse zu erleichtern und das Bewusstsein für die Vielfalt der Maße zu schärfen, entwickeln wir ein Python-Paket mit dem Namen pydistinto. Mithilfe von pydistinto können Nutzer:innen auch mit geringen Programmier- und Statistikkenntnissen zwei Textkorpora mit unterschiedlichen Maßen miteinander vergleichen, und in einem fortgeschrittenen Modus auch die Eigenschaften und Leistungsfähigkeit der unterschiedlichen Maße empirisch ermitteln und gegenüberstellen. Durch Tabellen und Abbildungen werden in dem geplanten Poster vor allem die folgenden Aspekte unseres Pakets vorgestellt: die Möglichkeiten der Vorverarbeitung der Textdaten, die implementierten Distinktivitätsmaße und die Visualisierung der kontrastiven Analyseergebnisse. Ein Beitrag zur 8. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum" - DHd 2022 Kulturen des digitalen Gedächtnisses.

Das Abstract ist Teil der vDHd Konferenz 2021 "Experimente", einer durch die Community des Verbandes "Digital Humanities im deutschsprachigen Raum" e.V. organisierten, virtuellen Konferenz. Die Abstracts haben kein Peer-Review-Verfahren durchlaufen.

There is a set of statistical measures developed mostly in corpus and computational linguistics and information retrieval, known as keyness measures, which are generally expected to detect textual features that account for differences between two texts or groups of texts. These measures are based on the frequency, distribution, or dispersion of words (or other features). Searching for relevant differences or similarities between two text groups is also an activity that is characteristic of traditional literary studies, whenever two authors, two periods in the work of one author, two historical periods or two literary genres are to be compared. Therefore, applying quantitative procedures in order to search for differences seems to be promising in the field of computational literary studies as it allows to analyze large corpora and to base historical hypotheses on differences between authors, genres and periods on larger empirical evidence. However, applying quantitative procedures in order to answer questions relevant to literary studies in many cases raises methodological problems, which have been discussed on a more general level in the context of integrating or triangulating quantitative and qualitative methods in mixed methods research of the social sciences. This paper aims to solve these methodological issues concretely for the concept of distinctiveness and thus to lay the methodological foundation permitting to operationalize quantitative procedures in order to use them not only as rough exploratory tools, but in a hermeneutically meaningful way for research in literary studies. Based on a structural definition of potential candidate measures for analyzing distinctiveness in the first section, we offer a systematic description of the issue of integrating quantitative procedures into a hermeneutically meaningful understanding of distinctiveness by distinguishing its epistemological from the methodological perspective. The second section develops a systematic strategy to solve the methodological side of this issue based on a critical reconstruction of the widespread non-integrative strategy in research on keyness measures that can be traced back to Rudolf Carnap’s model of explication. We demonstrate that it is, in the first instance, mandatory to gain a comprehensive qualitative understanding of the actual task. We show that Carnap’s model of explication suffers from a shortcoming that consists in ignoring the need for a systematic comparison of what he calls the explicatum and the explicandum. Only if there is a method of systematic comparison, the next task, namely that of evaluation can be addressed, which verifies whether the output of a quantitative procedure corresponds to the qualitative expectation that must be clarified in advance. We claim that evaluation is necessary for integrating quantitative procedures to a qualitative understanding of distinctiveness. Our reconstruction shows that both steps are usually skipped in empirical research on keyness measures that are the most important point of reference for the development of a measure of distinctiveness. Evaluation, which in turn requires thorough explication and conceptual clarification, needs to be employed to verify this relation. In the third section we offer a qualitative clarification of the concept of distinctiveness by spanning a three-dimensional conceptual space. This flexible framework takes into account that there is no single and proper concept of distinctiveness but rather a field of possible meanings depending on research interest, theoretical framework, and access to the perceptibility or salience of textual features. Therefore, we shall, instead of stipulating any narrow and strict definition, take into account that each of these aspects – interest, theoretical framework, and access to perceptibility – represents one dimension of the heuristic space of possible uses of the concept of distinctiveness. The fourth section discusses two possible strategies of operationalization and evaluation that we consider to be complementary to the previously provided clarification, and that complete the task of establishing a candidate measure successfully as a measure of distinctiveness in a qualitatively ambitious sense. We demonstrate that two different general strategies are worth considering, depending on the respective notion of distinctiveness and the interest as elaborated in the third section. If the interest is merely taxonomic, classification tasks based on multi-class supervised machine learning are sufficient. If the interest is aesthetic, more complex and intricate evaluation strategies are required, which have to rely on a thorough conceptual clarification of the concept of distinctiveness, in particular on the idea of salience or perceptibility. The challenge here is to correlate perceivable complex features of texts such as plot, theme (aboutness), style, form, or roles and constellation of fictional characters with the unperceived frequency and distribution of word features that are calculated by candidate measures of distinctiveness. Existing research did not clarify, so far, how to correlate such complex features with individual word features. The paper concludes with a general reflection on the possibility of mixed methods research for computational literary studies in terms of explanatory power and exploratory use. As our strategy of combining explication and evaluation shows, integration should be understood as a strategy of combining two different perspectives on the object area: in our evaluation scenarios, that of empirical reader response and that of a specific quantitative procedure. This does not imply that measures of distinctiveness, which proved to reach explanatory power in one qualitative aspect, should be supposed to be successful in all fields of research. As long as evaluation is omitted, candidate measures of distinctiveness lack explanatory power and are limited to exploratory use. In contrast with a skepticism that has sometimes been expressed from literary scholars with regard to the relevance of computational literary studies on proper issues of the humanities, we believe that integrating computational methods into hermeneutic literary studies can be achieved in a way that reaches higher explanatory power than the usual exploratory use of keyness measures, but it can only be achieved individually for concrete tasks and not once and for all based on a general theoretical demonstration., See also the publisher version here, accessible via personal request: https://zenodo.org/record/5707377

In Corpus Linguistics, numerous statistical measures have been adopted to analyze large amounts of textual data in a contrastive perspective, in order to extract characteristic or “distinctive” features. While the most widely-used keyness measures are based on word frequency, an increasing number of research papers recently suggested dispersion-based measures as a better solution. These, however, are not new to Computational Literary Studies (CLS). In 2007, John Burrows introduced Zeta, a statistical measure that is mainly based on the degree of dispersion of a feature in a text corpus. In this paper, we also introduce Eta, a new measure of distinctiveness that is based on deviation of proportions suggested by Stefan Gries. By comparing Eta with Zeta, we demonstrate that both measures are able to identify relevant, interpretable distinctive words in a target corpus. Additionally, we make a first attempt to detect the key differences between these two measures by interpreting the top distinctive words. DFG Schwerpunktprogramm SPP 2207 "Computational Literary Studies" Online: https://gepris.dfg.de/gepris/projekt/402743989 https://dfg-spp-cls.github.io/ Teilprojekt: "Zeta und Konsorten. Distinktivitätsmaße für die Digitalen Literaturwissenschaften" Online: https://gepris.dfg.de/gepris/projekt/424211690 https://dfg-spp-cls.github.io/projects_en/2020/01/24/TP-Zeta_and_Company/ https://zeta-project.eu/de/

Statistical measures of distinctiveness allow researchers to extract features (e.g. words or parts-of-speech) that are characteristic or "distinctive" for a given group of texts compared to another group of texts. In order to reach a deeper understanding of measures of distinctiveness, we first analyze a significant range of existing measures of distinctiveness to determine and compare their statistical properties and integrate them into this joint conceptual model. DFG Schwerpunktprogramm SPP 2207 "Computational Literary Studies" Online: https://gepris.dfg.de/gepris/projekt/402743989 https://dfg-spp-cls.github.io/ Teilprojekt: "Zeta und Konsorten. Distinktivitätsmaße für die Digitalen Literaturwissenschaften" Online: https://gepris.dfg.de/gepris/projekt/424211690 https://dfg-spp-cls.github.io/projects_en/2020/01/24/TP-Zeta_and_Company/ https://zeta-project.eu/de/

L'articolo reinterpreta la categoria narratologica di onniscienza applicata al campo della non-fiction e dopo un preambolo teorico si esaminano due casi particolari della narrativa italiana contemporanea: "Città sommersa" di Marta Barone e "La natura è innocente" di Walter Siti The article reinterprets the narratological category of omniscience applied to the field of non-fiction and after a theoretical preamble we examine two particular cases of contemporary Italian fiction: "Submerged city" by Marta Barone and "Nature is innocent" by Walter Siti