36.3074, Confs: 10th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (Morocco)

[The LINGUIST List]

LINGUIST List: Vol-36-3074. Mon Oct 13 2025. ISSN: 1069 - 4875.

Subject: 36.3074, Confs: 10th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (Morocco)

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Date: 11-Oct-2025
From: Anna Kazantseva [anna at anna-kazantseva.com]
Subject: 10th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature


10th Joint SIGHUM Workshop on Computational Linguistics for Cultural
Heritage, Social Sciences, Humanities and Literature
Short Title: SIGHUM

Date: 24-Mar-2026 - 29-Mar-2026
Location: Rabat, Morocco
Contact: Anna Kazantseva
Contact Email: latech-clfl at googlegroups.com
Meeting URL:
https://sighum.wordpress.com/events/sighum-latech-clfl-2026/

Linguistic Field(s): Computational Linguistics

Submission Deadline: 05-Jan-2026

The 10th Joint SIGHUM Workshop on Computational Linguistics for
Cultural Heritage, Social Sciences, Humanities and Literature to be
held at EACL in March 2026 in Rabat, Morocco as a two-day workshop
with one on-site and one online day.
Organizers: Diego Alves, Yuri Bizzoni, Stefania Degaetano-Ortlieb,
Anna Kazantseva, Janis Pagel, Stan Szpakowicz
SIGHUM (LaTeCH-CLfL) 2026 is the tenth 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 events which also host the SIGHUM
business meetings.
Workshop site:
https://sighum.wordpress.com/events/sighum-latech-clfl-2026/
Deadlines:
Submission deadline: January 5th, 2026
Notification of acceptance: February 3rd, 2026
Camera-ready paper due: February 10th, 2026
Content and Topics:
The community of the broadly understood Digital Humanities (DH) has
witnessed remarkable growth and transformation, fueled by the rapid
advancements in NLP. There is 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. Even so, the heterogeneous landscape of the DH
with their diverse, often multi-lingual or multi-modal sources can be
a challenge for NLP. Consider, for example, the growing interest in
historical language data and in under-resourced languages.
There are unique obstacles in developing comprehensive language models
in aid of the linguistic diversity in DH. The handling of noisy and
non-standard data, and the need for domain adaptation and intensive
annotation, continue to be at the forefront of research effort in the
community. The literary studies, which have witnessed substantial
progress in the application of NLP methods, bring their own similar
problems. Navigating forms of creative expression requires more than
the typical information-seeking tools. A case in point might be the
study of literature of a certain period, author or sub-genre, the
recognition of certain literary devices, or the quantitative analysis
of poetry.
The emergence of Large Language Models (LLMs) expands the DH toolkit.
There is support for automatic text cleaning and annotation, creation
of semantic resources, analysis of narrative, genre and literary
style, and linking information across sources. LLMs can support
historical or low-resource languages, particularly when complemented
with domain-specific fine-tuning and careful evaluation. One must
note, however, that even with careful adaptation, curation and
attention to interpretability, LLM outputs remain prone to errors,
biases and lack of transparency; that requires rigorous assessment to
ensure their suitability for scholarly research.
There is growing emphasis on the importance of explanation in NLP
models. That applied equally to DH, whose various domains enjoy the
effect of NLP. Transparency and clarity of the results are critical if
one is to accept the processed data, and gain valuable insights. That
is why one must carefully consider a balance between raw performance
scores and interpretability, in keeping with the specific research
objectives.
For many years now, this broad research context has 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. It
encourages the Computational Linguistics community to build rich,
effective tools and, above all, interpretable models.
Topics:
Our workshops attract original work on 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;
 - interpretability of large language models output for DH-related
tasks (explainable AI);
 - linking and retrieving information from different sources, media,
and domains;
 - low-resource and historical language processing;
 - 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
Information for Authors:
We invite papers on original, unpublished work in the topic areas of
the workshop. We will consider long papers, 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 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 follow the ACL paper styles (for LaTeX /
Overleaf and MS Word) available at
https://github.com/acl-org/acl-style-files. Papers should be submitted
electronically, only in PDF, via the LaTeCH-CLfL 2026 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. Please keep references to your own work at a
reasonable minimum, and do not use anonymous citations.
In accordance with the EACL 2026 policy on multiple submission, we
will not consider any paper that is under review in a journal or
another conference at the time of submission. During the review
period, papers submitted to our workshop cannot also be submitted
elsewhere.



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