MONA – M odes o f N arration and A ttribution
MONA is the umbrella project for two externally funded interdisciplinary projects dealing with textual structures, phenomena of information attribution, and various aspects of narrative modes.
The DFG project "Structuring Literature: Variants and Functions of Reflective Passages in Narrative Fiction" aims to identify and classify author/narrator-attributed and character-attributed reflective passages in narrative fiction using machine learning approaches based on linguistics. In the process, we will determine and investigate the patterns of occurrence of these passages over the course of about 350 years of literary history. Although even non-experts can distinguish reflective passages in novels from passages that report on actions or describe characters, reflective passages have not yet been established as an independent research category in literary theory. This is where the project comes in, developing new computational models for recognizing reflective passages and related phenomena, while demonstrating the usefulness of these algorithmic methods for literary historiography.
To avoid communicative misunderstandings or even misinterpretations, it is important to know who is in charge of the information contained in a text, i.e., to whom information is attributed. We speak of uncertain attribution when a piece of information cannot be unambiguously assigned. In fictional texts, attributed instances can be the author, a narrator, and characters. The project "Uncertain Attribution: Attribution in Fictional and Factual Texts", funded by the Volkswagen Foundation, investigates three phenomena of uncertain attribution in literary texts, namely (i) reflective passages, (ii) free indirect discourse, and (iii) overt-narrator passages. The aim of the project is to develop computational models for the identification and interpretation of uncertain attribution.
MONA allows us to mutually relate the two projects to each other and to use synergies in terms of content and human resources that arise through teamwork. MONA thus brings literary studies, linguistics, and computer science even closer together. Besides highlighting the subject-specific interfaces of these three disciplines, the interdisciplinarity also helps to further develop discipline-specific assumptions and theories and to transfer them to the Digital Humanities.
Publications
- Anna Mareike Weimer, Florian Barth, Tillmann Dönicke, Luisa Gödeke, Hanna Varachkina, Anke Holler, Caroline Sporleder, and Benjamin Gittel (2022). The (In-)Consistency of Literary Concepts. Operationalising, Annotating and Detecting Literary Comment. Journal of Computational Literary Studies.
- Luisa Gödeke, Florian Barth, Tillmann Dönicke, Hanna Varachkina, Anna Mareike Weimer, Benjamin Gittel, Anke Holler, and Caroline Sporleder (2022). Generalisierungen als literarisches Phänomen. Charakterisierung, Annotation und automatische Erkennung. In: Zeitschrift für digitale Geisteswissenschaften.
- Tillmann Dönicke, Florian Barth, Hanna Varachkina, and Caroline Sporleder (2022). MONAPipe: Modes of Narration and Attribution Pipeline for German Computational Literary Studies and Language Analysis in spaCy. In Proceedings of the 18th Conference on Natural Language Processing (KONVENS 2022).
- Florian Barth, Hanna Varachkina, Tillmann Dönicke, and Luisa Gödeke (2022). Levels of Non-Fictionality in Fictional Texts. In Proceedings of the The 18th Joint ACL - ISO Workshop on Interoperable Semantic Annotation.
- Hanna Varachkina, Florian Barth, Luisa Gödeke, Anna Mareike Hofmann, and Tillmann Dönicke (2022). Reflexive Passagen und ihre Attribution. In: DHd 2022 Kulturen des digitalen Gedächtnisses. 8. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum" (DHd 2022).
- Benjamin Gittel (2022). Reflexive Passagen in fiktionaler Literatur. Überlegungen zu ihrer Identifikation und Funktion am Beispiel von Wielands „Geschichte des Agathon“ und Goethes „Wahlverwandtschaften“. In: Euphorion 116.
- Tillmann Dönicke, Hanna Varachkina, Anna Mareike Weimer, Luisa Gödeke, Florian Barth, Benjamin Gittel, Anke Holler, and Caroline Sporleder (2022). Modelling Speaker Attribution in Narrative Texts With Biased and Bias-Adjustable Neural Networks. Frontiers in Artificial Intelligence.
- Tillmann Dönicke, Luisa Gödeke, and Hanna Varachkina (2021). Annotating Quantified Phenomena in Complex Sentence Structures Using the Example of Generalising Statements in Literary Texts. In Proceedings of the 17th Joint ACL - ISO Workshop on Interoperable Semantic Annotation.
Resources
- Florian Barth, Tillmann Dönicke, Benjamin Gittel, Luisa Gödeke, Anna Mareike Weimer, Anke Holler, Caroline Sporleder, and Hanna Varachkina (2021). MONACO: Modes of Narration and Attribution Corpus.
- Tillmann Dönicke, Florian Barth, Hanna Varachkina, and others (2022). MONAPipe: Modes of Narration and Attribution Pipeline.