to be held mid-October 2022 in conjunction with COLING 2022 in Gyeongju, Republic of Korea.
First Call for Papers (with apologies for cross-posting)
Organisers: Stefania Degaetano-Ortlieb, Anna Kazantseva, Nils Reiter, Stan Szpakowicz
LaTeCH-CLfL 2022 is the sixth in a series of meetings for NLP researchers who work with data from the broadly understood arts, humanities and social sciences, and for specialists in those disciplines who apply NLP techniques in their work. The workshop continues a long tradition of annual meetings. The SIGHUM Workshops on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH) ran ten times in 2007-2016. The five Workshops on Computational Linguistics for Literature (CLfL) took place in 2012-2016. The first five joint workshops (LaTeCH-CLfL) were held in 2017-2021.
*Topics and Content*
In the Humanities, Social Sciences, Cultural Heritage and literary communities, there is increasing interest in, and demand for, NLP methods for semantic and structural annotation, intelligent linking, discovery, querying, cleaning and visualization of both primary and secondary data. This is even true of primarily non-textual collections, given that text is also the pervasive medium for metadata. Such applications pose new challenges for NLP research: noisy, non-standard textual or multi-modal input, historical languages, vague research concepts, multilingual parts within one document, and so no. Digital resources often have insufficient coverage; resource-intensive methods require (semi-)automatic processing tools and domain adaptation, or intense manual effort (e.g., annotation).
Literary texts bring their own problems, because navigating this form of creative expression requires more than the typical information-seeking tools. Examples of advanced tasks include the study of literature of a certain period, author or sub-genre, recognition of certain literary devices, or quantitative analysis of poetry.
NLP methods applied in this context not only need to achieve high performance, but are often applied as a first step in research or scholarly workflow. That is why it is crucial to interpret model results properly; model interpretability might be more important than raw performance scores, depending on the context.
More generally, there is a growing interest in computational models whose results can be used or interpreted in meaningful ways. It is, therefore, of mutual benefit that NLP experts, data specialists and Digital Humanities researchers who work in and across their domains get involved in the Computational Linguistics community and present their fundamental or applied research results. It has already been demonstrated how cross-disciplinary exchange not only supports work in the Humanities, Social Sciences, and Cultural Heritage communities but also promotes work in the Computational Linguistics community to build richer and more effective tools and models.
Topics of interest include, but are not limited to, the following:
• 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;
• linking and retrieving information from different sources, media, and domains;
• modelling dialogue literary style for generation;
• modelling of information and knowledge in the Humanities, Social Sciences, and Cultural Heritage;
• profiling and authorship attribution;
• search for scientific and/or scholarly literature;
• work with linguistic variation and non-standard or historical use of language.
*Information for Authors*
We invite papers on original, unpublished work in the topic areas of the workshop. In addition to long papers, we will consider short papers and system descriptions (demos). We also welcome position papers.
• Long papers, presenting completed work, may consist of up to eight (8) pages of content plus additional pages of references (just two if possible -:). The final camera-ready versions of accepted long papers will be given one additional page of content (up to 9 pages) so that reviewers’ comments can be taken into account.
• A short paper / demo presenting work in progress, or the description of a system, and may consist of up to four (4) pages of content plus additional pages of references (one if you can). Upon acceptance, short papers will be given five (5) content pages in the proceedings.
• A position paper — clearly marked as such — should not exceed eight (8) pages including references.
All submissions are to use the COLING stylesheets (for LaTeX / Overleaf and MS Word), to be posted soon at https://coling2022.org/. Papers should be submitted electronically, only in PDF, via the LaTeCH-CLfL2022 submission website on the Softconf pages (we will publish the link as soon as we have it).
Reviewing will be double-blind. Please do not include the authors’ names and affiliations, or any references to Web sites, project names, acknowledgements and so on — anything that immediately reveals the authors’ identity. Self-references should be kept to a reasonable minimum, and anonymous citations cannot be used.
Accepted papers will be published in the workshop proceedings available as usual in the ACL Anthology.
*Important Dates* (still tentative :-<)
Papers due: July 11, 2022 Notification of acceptance: August 22, 2022 Camera-ready papers due: September 5, 2022 Workshop: one day in the period October 12-17, 2022
*More on the organisers*
Stefania Degaetano-Ortlieb, Language Science and Technology, Saarland University Anna Kazantseva, National Research Council Canada Nils Reiter, Department for Digital Humanities, University of Cologne Stan Szpakowicz, School of Electrical Engineering and Computer Science, University of Ottawa
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