This dataset contains all the results (including reconstructed texts, similarity scores etc.) of the reconstrution of DTF texts. The work is presented at the 4th Annual Conference of Computational Literary Studies, Krakow 2025. This dataset is also available in this GitHub repository. This work was created in the context of the work of the association German National Research Data Infrastructure (NFDI) e.V. NFDI is financed by the Federal Republic of Germany and the 16 federal states, and the consortium Text+ is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project number 460033370. The authors would like to thank for the funding and support. Furthermore, thanks also include all institutions and actors who are committed to the association and its goals.
New article in #JCLS 4(1)! 🎉
Du, Ackerschewski, Navruz, Sınır, Valline & @christof : “Reconstructing Shuffled Text. Bad Results for #NLP, but Good News for Using #In-Copyright Texts” https://doi.org/10.48694/jcls.4163
#CLS #DTF #LiteraryComputing #CCLS25 #OpenScience #Copyright
Narrative structure in fiction relies on the strategic presentation of events, where the ordering and disclosure of information (syuzhet) shape reader engagement and tension. This study outlines a computational model for detecting syuzhet by formalizing event annotation in fictional texts across multiple languages. While automated event detection has been widely applied in domains like journalism and history, its theoretical foundations remain fragmented due to divergent definitions of "event" and domain-specific priorities. We critically synthesize prior approaches, highlighting their methodological and applicative distinctions, and position our model within this landscape. Additionally, we demonstrate its adaptability by comparing it to a storyline analysis framework designed for news, revealing cross-domain utility. Our work offers a flexible computational narratology framework for analyzing narrative progression in both fiction and non-fiction contexts.
JCLS