The community of the broadly understood Digital Humanities has a steady interest in, and a high demand for, NLP methods of semantic and structural annotation, intelligent linking, discovery, querying, cleaning and visualization of primary and secondary data, as well as the processing of textual metadata in mainly non-textual collections. All this is a constant challenge for NLP, considering the noisy, non-standard and often multi-modal input, historical (usually under-resourced) languages, imprecise research concepts, multilinguality in documents, and so on. Digital resources often have low coverage. Resource-intensive methods require automatic or semi-automatic processing tools and domain adaptation, or intensive annotation effort.
Literary studies bring their own problems: navigating forms of creative expression requires more than the typical information-seeking tools. Consider, for example, the study of literature of a certain period, author or sub-genre, the recognition of certain literary devices, or the quantitative analysis of poetry.
NLP methods for such a broad area of Digital Humanities are often applied as a first step in research or scholarly workflow. It is essential to interpret model results properly; interpretability might be more important than raw performance scores, depending on the circumstances.
This broad research context has for many years drawn together NLP experts, data specialists and researchers in Digital Humanities who work in and across their domains. Our long-standing series of workshops has shown that cross-disciplinary exchange supports work in the Humanities, Social Sciences, and Cultural Heritage communities, and encourages the Computational Linguistics community to build richer and more effective tools and models.
We invite original, unpublished work. Our workshops attract a wide variety of topics, including (but as usual not restricted to) these:
- adaptation of NLP tools to Cultural Heritage, Social Sciences, Humanities and literature;
- automatic error detection and cleaning of textual data;
- complex annotation schemas, tools and interfaces;
- creation (fully- or semi-automatic) of semantic resources;
- creation and analysis of social networks of literary characters;
- discourse and narrative analysis/modelling, notably in literature;
- emotion analysis for the humanities and for literature;
- generation of literary narrative, dialogue or poetry;
- identification and analysis of literary genres;
- information/knowledge modelling in the Humanities, Social Sciences and Cultural Heritage;
- linking and retrieving information from different sources, media, and domains;
- modelling dialogue literary style for generation;
- profiling and authorship attribution;
- search for scientific and/or scholarly literature;
- work with linguistic variation and non-standard or historical use of language.