34.145, Calls: Computational Linguistics, Ling & Literature, Text/Corpus Linguistics/Croatia

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Tue Jan 17 17:29:03 UTC 2023


LINGUIST List: Vol-34-145. Tue Jan 17 2023. ISSN: 1069 - 4875.

Subject: 34.145, Calls: Computational Linguistics, Ling & Literature, Text/Corpus Linguistics/Croatia

Moderators:

Editor for this issue: Everett Green <everett at linguistlist.org>
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Date: Tue, 17 Jan 2023 17:25:09
From: Anna Kazantseva [anna.kazantseva at nrc-cnrc.gc.ca]
Subject: LaTeCH-CLfL 2023: The 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

 
Full Title: LaTeCH-CLfL 2023: The 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 
Short Title: LaTeCH-CLfL 2023 

Date: 02-May-2023 - 06-May-2023
Location: Dubrovnik, Croatia 
Contact Person: Anna Kazantseva
Meeting Email: latech-clfl at googlegroups.com
Web Site: https://sighum.wordpress.com/events/latech-clfl-2023/ 

Linguistic Field(s): Computational Linguistics; Ling & Literature; Text/Corpus Linguistics 

Call Deadline: 13-Feb-2023 

Meeting Description:

LaTeCH-CLfL 2023 is the seventh 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 six joint workshops (LaTeCH-CLfL) were held in 2017-2022.


Call for Papers:

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.

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 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 EACL stylesheets (for LaTeX / Overleaf and MS
Word), posted at https://2023.eacl.org/calls/styles. Papers should be
submitted electronically, only in PDF, via the LaTeCH-CLfL2023 submission
website on the SoftConf pages (we will publish the link as soon as we have
it).

Reviewing will be double-blind.

Important Dates (tentative)

Papers due: February 13, 2023
Notification of acceptance: March 13, 2023
Camera-ready papers due: March 27, 2023
Workshop date: May 2 or May 6, 2023

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

Contact

latech-clfl at googlegroups.com




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