February 2026 - Corpora

Multimodal sexism identification with sensor data: CFP EXIST 2026
by JORGE AMANDO CARRILLO DE ALBORNOZ CUADRADO 02 Feb '26

02 Feb '26

Please consider contributing and/or forwarding to appropriate colleagues and groups. ****We apologize for the multiple copies of this e-mail**** ---------------------------------------------------------------------------------------------------- Call for Participation ---------------------------------------------------------------------------------------------------- First Call for Participation: EXIST 2026: Multimodal sexism identification with sensor data Website: http://nlp.uned.es/exist2026/ EXIST is a series of scientific events and shared tasks on sexism identification in social networks. EXIST aims to foster the automatic detection of sexism in a broad sense, from explicit misogyny to other subtle expressions that involve implicit sexist behaviours (EXIST 2021, EXIST 2022, EXIST 2023, EXIST 2024, EXIST 2025). The sixth edition of the EXIST shared task will be held as a Lab in CLEF 2026, on September 21-24, 2026, at Friedrich-Schiller-Universität Jena, Germany . In EXIST 2026, we take a significant step forward by integrating the principles of Human-Centered AI (HCAI) into the development of automatic tools for detecting sexism online. Recognizing that no single interpretation can fully capture the diversity of human perception, we go beyond traditional annotation paradigms by combining Learning With Disagreement (LeWiDi) with sensor-based data (EEG, heart rate, and eye-tracking signals) collected from subjects exposed to potentially sexist content, with the aim of capturing unconscious responses to sexism. This dual approach represents a breakthrough in dataset creation for sensitive and value-laden tasks: for the first time, datasets will include not only divergent judgments from annotators, but also the embodied traces of how this content affect. This richer, multidimensional annotation process will enable the development of more inclusive, equitable, and socially aware AI systems for detecting sexism in complex multimedia formats like memes and short videos, where ambiguity and affect play a critical role. Similar to the approaches in the 2023, 2024 and 2025 edition, this edition will also embrace the Learning With Disagreement (LeWiDi) paradigm for both the development of the dataset and the evaluation of the systems. The LeWiDi paradigm doesn’t rely on a single “correct” label for each example. Instead, the model is trained to handle and learn from conflicting or diverse annotations. This enables the system to consider various annotators’ perspectives, biases, or interpretations, resulting in a fairer learning process. Building upon the EXIST 2025 dataset, this edition focuses exclusively on multimedia formats, comprising six experimental subtasks applied to images (memes) and videos (TikToks). Participants are challenged to address three main objectives: sexism identification (x.1), source intention detection (x.2), and sexism categorization (x.3) (numbering of subtask is consistent with EXIST 2025). Participants will be asked to classify memes and videos (in English and Spanish) according to the following tasks: TASK 2: Sexism detection in Memes: TASK 2.1 - Sexism Identification in Memes: this is a binary classification subtask consisting on determining wheter a meme describes a sexist situation or criticizes a sexist behaviour, and classifying it into two categories: YES and NO. Task 2.2: Source Intention in Memes: this subtask aims to categorize the meme according to the intention of the author. Due to the characteristics of the memes systems should only classify memes into the DIRECT or JUDGEMENTAL categories. Task 2.3: Sexism Categorization in Memes: once a message has been classified as sexist, the third subtask aims to categorize the message in different types of sexism (according to a categorization proposed by experts and that takes into account the different facets of women that are undermined). In particular, each sexist tweet must be categorized in one or more of the following categories: (i) IDEOLOGICAL AND INEQUALITY, (ii) STEREOTYPING AND DOMINANCE, (iii) OBJECTIFICATION, (iv) SEXUAL VIOLENCE and (v) MISOGYNY AND NON-SEXUAL VIOLENCE. TASK 3: Sexism detection in Videos: SUBTASK 3.1 - Sexism Identification in Videos: this is a binary classification task as in Subtasks 2.1. SUBTASK 3.2: Source Intention in Videos: this subtask replicates subtask 2.2 for memes, but it takes as source videos. SUBTASK 3.3: This subtask aims to classify sexist videos according to the categorization provided for Subtask 2.3: (i) IDEOLOGICAL AND INEQUALITY, (ii) STEREOTYPING AND DOMINANCE, (iii) OBJECTIFICATION, (iv) SEXUAL VIOLENCE and (v) MISOGYNY AND NON-SEXUAL VIOLENCE. Although we recommend to participate in all subtasks and in both languages, participants are allowed to participate just in one of them (e.g. subtask 2.1) and in one language (e.g. English). During the training phase, the task organizers will provide the participants with the manually-annotated EXIST 2026 dataset. For the evaluation of the systems, the unlabeled test data will be released. We encourage participation from both academic institutions and industrial organizations. We invite participants to register for the lab at CLEF 2026 Labs Registration site (https://clef-labs-registration.dipintra.it/). You will receive information about how to join the Discord Group for the EXIST 2026 shared task. Important Dates: * 17 November 2025: Registration opens. * 26 February 2026: Training set available. * 9 April 2026: Test set available. * 23 April 2026: Registration closes. * 7 May 2026: Runs submission due to organizers. * 28 May 2026: Results notification to participants. * 4 June 2026: Submission of Working Notes by participants. * 30 June 2026: Notification of acceptance (peer reviews). * 6 July 2026: Camera-ready participant papers due to organizers. * 21-24 September 2026: EXIST 2026 at CLEF Conference. ** Note: All deadlines are 11:59PM UTC-12:00 ("anywhere on Earth") ** Organizers: Laura Plaza, Universidad Nacional de Educación a Distancia (UNED) Jorge Carrillo-de-Albornoz, Universidad Nacional de Educación a Distancia (UNED) Iván Arcos, Universitat Politècnica de València (UPV) Maria Aloy Mayo, Universitat Politècnica de València (UPV) Paolo Rosso, Universitat Politècnica de València (UPV) Damiano Spina, Royal Melbourne Institute of Technology (RMIT) Contact: Contact the organizers by writing to: jcalbornoz(a)lsi.uned.es Website: http://nlp.uned.es/exist2026/ AVISO LEGAL. Este mensaje puede contener información reservada y confidencial. Si usted no es el destinatario no está autorizado a copiar, reproducir o distribuir este mensaje ni su contenido. Si ha recibido este mensaje por error, le rogamos que lo notifique al remitente. Le informamos de que sus datos personales, que puedan constar en este mensaje, serán tratados en calidad de responsable de tratamiento por la UNIVERSIDAD NACIONAL DE EDUCACIÓN A DISTANCIA (UNED) c/ Bravo Murillo, 38, 28015-MADRID-, con la finalidad de mantener el contacto con usted. La base jurídica que legitima este tratamiento, será su consentimiento, el interés legítimo o la necesidad para gestionar una relación contractual o similar. En cualquier momento podrá ejercer sus derechos de acceso, rectificación, supresión, oposición, limitación al tratamiento o portabilidad de los datos, ante la UNED, Oficina de Protección de datos<https://www.uned.es/dpj>, o a través de la Sede electrónica<https://uned.sede.gob.es/> de la Universidad. Para más información visite nuestra Política de Privacidad<https://descargas.uned.es/publico/pdf/Politica_privacidad_UNED.pdf>.

1 0

3rd Int. Workshop on Natural Scientific Language Processing (NSLP) 2026: 3rd CFP
by Georg Rehm 02 Feb '26

02 Feb '26

3rd International Workshop on Natural Scientific Language Processing (NSLP 2026) 12 May 2026 – Co-located with LREC 2026 Palma, Mallorca (Spain) NSLP 2026 features two shared tasks: * ClimateCheck 2026: Scientific Fact-Checking of Social Media Claims * SOMD 2026: Software Mention Detection & Coreference Resolution NSLP 2026 – important dates: * Submission deadline: 20 February 2026 * Notifications: 13 March 2026 * Camera-ready: 30 March 2026 NSLP 2026 website (including the shared tasks): * https://nfdi4ds.github.io/nslp2026 Scientific research has witnessed a steep growth rate over the last decades. The number of scholarly publications is growing exponentially, and doubles every 15-17 years. Consequently, both general and specialised repositories, databases, knowledge graphs, and digital libraries have been developed to publish and manage scientific artifacts. Examples include the Open Research Knowledge Graph (ORKG), the Semantic Scholar Academic Graph (S2AG), PubMed Central and also the ACL Anthology. These resources enable the collection, reuse, tracking, and expansion of scientific findings, and facilitate downstream applications such as scientific search engines. However, in order to develop robust systems that deal with scholarly text, various challenges need to be addressed. The current status quo of scientific communication mostly includes scholarly articles as unstructured PDF documents, which are not machine-readable in the sense that relevant scientific information can be extracted easily, thus making extracting and utilising this information as part of the scientific process a laborious and time-consuming task. Developing methods for converting unstructured information into structured formats is one of the major challenges in the field of Natural Scientific Language Processing (NSLP). This goal encompasses related challenges such as detecting, disambiguating, and linking mentions of scientific artifacts (e.g., software tools or specific datasets or language resources), and tracking state-of-the-art models and their evaluation scores (including new versions of existing models). Extracting and managing heterogeneous scientific knowledge effectively remains a challenging ongoing research area. Existing efforts are often fragmented, addressing separate issues with distinct datasets and conceptual approaches. NSLP 2026 addresses current topics and issues in Natural Scientific Language Processing. It is proposed and organised with the support of NFDI for Data Science and Artificial Intelligence (NFDI4DS), a long-term project with approx. 20 partners who work towards building a German national research data infrastructure for DS and AI. The workshop aims to further bring together the international community of researchers who work on NSLP and related topics (including research knowledge graphs), to discuss current issues and possible solutions. NSLP 2026 includes two keynote speakers and presentations of accepted papers (oral and poster presentations), as well as three shared tasks. Topics of interest include, but are not limited to * Scientific LLMs – LLMs for NSLP * Language resources (LRs) and Language technologies (LTs) for NSLP beyond LLMs * Research Knowledge Graphs (RKGs), Scientific Knowledge Graphs (SKGs) and other forms of structured representation of research-related knowledge * Information extraction from scholarly articles * Extraction of research information from texts * Detection and disambiguation of mentions of datasets, tasks, software or other methods * Classification of scholarly articles (collections, single documents, parts of documents) * Information extraction for RKGs * Summarisation of scholarly articles * Scholarly IR and scientific search engines * Question answering over scientific knowledge * Metadata and cataloging * Cross-lingual and multilingual natural scientific language processing * Adaptation of NLP methods for NSLP purposes Important Dates * Paper submission deadline: 20 February 2026 (not to be extended) * Notification of acceptance: 13 March 2026 * Camera-ready submission: 30 March 2026 * Workshop: 12 May 2026 Submission Guidelines The NSLP 2026 workshop invites submissions of: regular long papers; short papers; position papers. We especially encourage submissions from junior researchers and students from diverse backgrounds. * Note that we will not accept work that is under review or has already been published in or accepted for publication in a journal, another conference, or another workshop. * The workshop invites anonymous submissions of regular long papers (up to 8 pages without references and appendix); short papers as well as position papers (up to 4 pages without references and appendix) presenting, for example, negative results, in-progress projects, or demos. * Authors are permitted to include an optional appendix of up to 2 pages. However, reviewers will not be mandated to review the appendix and all papers must be self-contained. * Reviewing will be performed double-blind, i.e., submissions must be anonymous. Reviewers will not actively try to identify the authors. * Submissions must be in PDF, formatted in the LREC 2026 style. * The proceedings of this workshop will be published in the ACL Anthology (full Open Access) as part of the LREC 2026 proceedings. * At least one author per contribution must register for the workshop for presentation. * All submissions are done via START: https://softconf.com/lrec2026/NSLP2026/ When submitting a paper through START, the authors will be asked to provide essential information about resources (in a broad sense, i.e., also technologies, standards, evaluation kits, etc.) that have been used for the work described in the paper or are a new result of your research. Moreover, ELRA encourages all LREC authors to share the described LRs (data, tools, services, etc.) to enable their reuse and replicability of experiments (including evaluation ones). Keynote Speakers * Iryna Gurevych, TU Darmstadt, Germany * Yufang Hou, ITU Austria, Austria Shared Tasks 1. ClimateCheck 2026: Scientific Fact-Checking of Social Media Claims The rise of climate discourse on social media offers new channels for public engagement but also amplifies mis- and disinformation. As online platforms increasingly shape public understanding of science, tools that ground claims in trustworthy, peer-reviewed evidence are necessary. The new iteration of ClimateCheck builds on the results and insights from the 2025 iteration (run at SDP 2025/ACL 2025), offering the following subtasks: Subtask 1: Abstract retrieval and claim verification: given a claim and corpus of publications, retrieve the top 10 most relevant abstracts and classify each claim-abstract pair as supports, refutes, or not enough information. Subtask 2: Disinformation narrative classification: given a claim, predict which climate disinformation narrative exists according to a predefined taxonomy. New training data will be released for both tasks, with task 1 having triple the amount of the last iteration. The new iteration will focus on sustainability, emphasising the need to build climate-friendly NLP systems with minimal environmental impact. Shared task co-organisers: Raia Abu Ahmad, Aida Usmanova, Max Upravitelev, Georg Rehm 2. SOMD 2026: Software Mention Detection & Coreference Resolution Understanding software mentions is crucial for reproducibility and to interpret experimental results. Citations of software are often informal, lacking the use of persistent identifiers, making it hard to infer and disambiguate knowledge about software efficiently. This task will build on SOMD 2025 (run at SDP 2025, co-located with ACL 2025) and focus on entity disambiguation as an under-investigated problem in this context. More precisely, we address the task of coreference resolution of software mentions across multiple documents, i.e. given a set of software mentions extracted from multiple scientific publications, cluster these mentions so that all software mentions in a particular cluster refer to the same real world software. We define three subtasks with varying challenges: Subtask 1: Software coreference resolution over gold standard mentions. Addresses the task based on high-quality (gold standard) mentions of software that are expert-annotated in multiple publications. Subtask 2: Software coreference resolution over predicted mentions. Addresses the task on software mentions that are automatically extracted using a baseline model, i.e. reflecting a typical information extraction scenario, where upstream pipelines (such as entity and metadata extraction) are imperfect. Subtask 3: Software coreference resolution at scale. Addresses the task using predicted mentions of software and metadata at a larger scale. This challenges models to scale effectively, maintain accuracy, and distinguish among an increasingly dense field of similar or overlapping software mentions. Shared task co-organisers: Sharmila Upadhyaya, Stefan Dietze, Frank Krüger, Wolfgang Otto Organisers * Georg Rehm (Deutsches Forschungszentrum für Künstliche Intelligenz & Humboldt-Universität zu Berlin, Germany) – main contact: <georg.rehm(a)dfki.de<mailto:georg.rehm@dfki.de>> * Stefan Dietze (GESIS Leibniz Institut für Sozialwissenschaften, Cologne & Heinrich-Heine-University Düsseldorf, Germany) * Danilo Dessí (University of Sharjah, UAE) * Diana Maynard (University of Sheffield, UK) * Sonja Schimmler (Technical University of Berlin & Fraunhofer FOKUS, Germany) Programme Committee * Marcel Ackermann, Lernzentrum Informatik (LZI), DBLP, Germany * Raia Abu Ahmad, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Germany * Tilahun Abedissa Taffa, University of Hamburg, Germany * Ekaterina Borisova, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Germany * Davide Buscaldi, LIPN, CNRS, University Paris 13, France * Leyla Jael Castro, ZB MED Information Centre for Life Sciences, Germany * Mathieu d’Aquin, Université de Lorraine, France * Jennifer D’Souza, TIB Leibniz Information Centre for Science and Technology, Germany * Catherine Faron, Université Côte d’Azur, France * Dayne Freitag, SRI International, USA * Paul Groth, University of Amsterdam, TheNetherlands * Leonhard Hennig, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Germany * Inma Hernandez, University of Seville, Spain * Robert Jäschke, Humboldt University of Berlin, Germany * Petr Knoth, Open University, UK * Frank Krüger, Wismar University of Applied Sciences, Germany * Julia Lane, NYU Wagner Graduate School of Public Service, USA * Andrea Mannocci, CNR-ISTI, Italy * Natalia Manola, OpenAIRE, Greece * Mirko Marras, University of Cagliari, Italy * Philipp Mayr-Schlegel, GESIS Leibniz-Institute for the Social Sciences, Germany * Pedro Ortiz Suarez, Common Crawl Foundation, USA * Wolfgang Otto, GESIS Leibniz-Institute for the Social Sciences, Germany * Haris Papageorgiou, R.C. Athena, Greece * Silvio Peroni, University of Bologna, Italy * Simone Ponzetto, Univ. of Mannheim, Germany * Diego Reforgiato Recupero, University of Cagliari, Italy * Harald Sack, FIZ Karlsruhe, Germany * Angelo Salatino, The Open University, UK * Philipp Schaer, TH Köln (University of Applied Sciences), Germany * Atsuhiro Takasu, University of Tokyo, Japan * Stefani Tsaneva, WU Wien, Austria * Ricardo Usbeck, Leuphana University, Germany * Thanasis Vergoulis, R.C. Athena, Greece

1 0

2nd [CFP] NLP4Ecology 2026 @ LREC 2026
by fr.grasso＠unito.it 02 Feb '26

02 Feb '26

We invite submissions to the 2nd Workshop on Ecology, Environment, and Natural Language Processing. We are particularly interested in contributions that push the boundaries of linguistics and NLP research in the context of ecological and environmental crisis and that foster interdisciplinary collaboration. Important Dates (All deadlines are 23:59 AoE.) Paper Submission Deadline: 27 February 2026 Notification of Acceptance: 20 March 2026 Camera-Ready Deadline: 30 March 2026 Workshop Date: 12 May 2026 (afternoon) Submissions will be handled via START Conference Manager. Please click the following link to access the submission system: https://softconf.com/lrec2026/NLP4Ecology2026/ The topics of interest include, but are not limited to: - Sentiment, Argument, and Stance Analysis of Environmental Topics: Evaluating public opinions, emotions, and stances on ecological issues across social media, news outlets, and other media, including environmental activism communication and AI–environment debates (e.g., Longo and Longo, 2025; Ibrohim et al., 2023; Barz et al., 2025; Grasso et al., 2024). - Automated Linguistic and Discourse/Frame Analysis, and Topic Modeling: Studying grammatical, lexical, and discourse patterns in ecological communication from an ecolinguistic perspective, including topic modeling and framing analyses of media, political discourse, corporate reports, and NGO communication (e.g., Widanti, 2022; Dehler-Holland et al., 2021; Bosco et al., 2025; Grasso et al., 2025b). - Detection of Anthropocentric and Speciesist Biases: Identifying and mitigating anthropocentric and speciesist biases in language data and NLP applications, including bias in large language models (e.g., Leach et al., 2021; Takeshita et al., 2022; Grasso et al., 2025a). - Text Classification, Entity Recognition, and Environmental Monitoring: Categorizing texts into environmental subdomains such as biodiversity, climate change, and conservation, and identifying or tracking mentions of species, habitats, pollutants, and ecological phenomena, including applications of LLMs to ecological and biodiversity corpora (e.g., Volkanovska, 2025; Schimanski et al., 2023; Abdelmageed et al., 2022; Grasso & Locci, 2024). - Fact-checking and Greenwashing Detection: Analyzing corporate sustainability reports and institutional communication to detect misleading claims, greenwashing practices, and inaccuracies in environmental discourse (e.g., Glazkova and Zakharova, 2025; Cojoianu et al., 2020; Moodaley & Telukdarie, 2023). - Ecofeminism, Environmental Justice, and Language: Exploring the intersections of gender, justice, power, and ecological narratives, and how NLP methods can support the analysis of environmental justice–oriented discourse. Further topics include: - Ecolinguistic applications of NLP. -Large Language Models (LLMs) application in Climate Change and Environmental domain. -Analysis of harmful environmental narratives and misinformation on social media. -Corpora creation and annotation for ecological discourse -Geo-tagging and Sentiment Mapping of Environmental Discussions -Fairness, ethics, and accountability in environmental NLP. -Environmental communication in low-resource languages. -Multimodal analysis for ecological and environmental challenges. -Lexical and semantic analysis of sustainability discourse. -Linked Data and Knowledge Graphs on ecological topics. -Language diversity and inclusion in environmental narratives. -Cognitive models and ecological storytelling. -NLP for understanding indigenous knowledge in environmental contexts. -Machine learning techniques for analyzing environmental communication. -NLP for environmental legislation and policy discourse. -NLP for environmental education and awareness campaigns. -Speech technologies to support ecological field research. -Educational chatbots and conversational agents for raising environmental awareness. We invite submissions in the following categories: - Regular Papers (from a minimum of 4 up to 8 pages) - Non-archival contributions (up to 4 pages). Regular papers must report original, previously unpublished work and follow the LREC 2026 Author Kit. Accepted regular papers will be included in the workshop proceedings. Non-archival contributions include research communications (i.e. work already published at other venues), work in progress, manifestos, and similar contributions. Non-archival contributions can be presented at the workshop but will not be included in the proceedings. Please visit https://nlp4ecology2026.di.unito.it/ for more infos. Contact e-mail: nlp4ecology.workshop(a)gmail.com

1 0

KG–LLM @ LREC 2026 - Knowledge Graphs and Large Language Models - Second Call for Paper
by Gilles Sérasset 02 Feb '26

02 Feb '26

Knowledge Graphs and Large Language Models (KG–LLM 2026) @ LREC 2026 We are pleased to announce the Workshop on Knowledge Graphs and Large Language Models (KG–LLM 2026), to be held in conjunction with LREC 2026 in Palma de Mallorca, Spain, May 16th 2026. We invite submissions of original research that leverages both Knowledge Graphs (KGs) and Large Language Models (LLMs) in any domain of Natural Language Processing or language resource development. More information at https://kg-llm.github.io/ Workshop Overview Large Language Models have become foundational in NLP, yet they continue to face challenges related to bias, hallucination, explainability, environmental impact, and the cost of training. Knowledge Graphs, in contrast, provide high-quality, interpretable, and reusable ontological and linguistic structures that support reasoning, fact checking, and knowledge preservation. The goal of this workshop is to bring together researchers working at the intersection of these two paradigms, exploring how explicit knowledge and implicit statistical learning can enhance each other. We welcome contributions that investigate, demonstrate, or evaluate systems, methods, or resources integrating both KGs and LLMs. Topics of Interest We encourage submissions on (but not limited to): 1. LLMs for Knowledge Graph Engineering KG modelling, resource creation, and interlinking Relation extraction Corpus annotation Ontology localization Creation or expansion of linguistic or knowledge graphs KG querying and question answering 2. Knowledge Graphs for Large Language Models Using linguistic or knowledge graphs as training data Fine-tuning LLMs using linked linguistic (meta)data Knowledge/linguistic graph embeddings KGs for model explainability, provenance, and source attribution Neural models for under-resourced languages KG-augmented RAG (KG-RAG) 3. Joint Use of KGs and LLMs in Applications Combined KG–LLM use cases with structured linguistic data Digital humanities applications Question answering over graph data Fake news and misinformation detection Educational applications and assisted learning Visualizing academic writing with KGs and LLMs KG-enhanced chatbots for health and medical contexts Application Domains All application domains are welcome (Digital Humanities, FinTech, Linguistics, Education, Cybersecurity, etc.) as long as the work uses both Knowledge Graphs and Large Language Models. Submission Guidelines Submission Format: Papers up to 8 pages excluding references. Style: All submissions must follow the LREC 2026 format and use the official LREC author kit. (available at https://lrec2026.info/authors-kit/ ) Review Process: Double-blind peer review. Submissions must be fully anonymized. Submission System: Papers must be submitted via the START conference system at https://softconf.com/lrec2026/KGLLM/ Language Resources: In line with LREC policies, authors are encouraged to describe, document, and share language resources, datasets, models, evaluation tools, or annotation guidelines used or created in their work. Accepted Papers: All accepted papers will be included in the LREC 2026 workshop proceedings. Presentation: Accepted papers will be presented as oral or poster sessions during the workshop. Important Dates *All deadlines are 11:59PM UTC-12:00 (“anywhere on Earth”)* Paper submission deadline: 26 February 2026 Notification to authors: 24 March 2026 Camera-ready due: 30 March 2026 Workshop date: 16 May 2026 Contact For questions, please contact the workshop organizers at: kg-llm-26(a)googlegroups.com Organizing Committee Gilles Sérasset, Université Grenoble Alpes, France Katerina Gkirtzou, Athena Research Center, Greece Michael Cochez, Ellis Institute Finland & Åbo Akademi, Finland Jan-Christoph Kalo, University of Amsterdam, Netherlands

1 0

📢 Call for Participation: Shared Task at Argument Mining Workshop, ACL2026
by anastassia.shaitarova＠uzh.ch 02 Feb '26

02 Feb '26

We invite the broader NLP community to participate in our Shared Task at The 13th Argument Mining and Reasoning Workshop, co-hosted with ACL2026 in San Diego 🏖️ , United States! https://argmining-org.github.io/2026/index.html#shared_task The shared task focuses on understanding argumentative structure in highly formal, legal-political United Nations resolutions. Participants are expected to build LLM-based systems to: 1) identify and classify argumentative paragraphs in preambles and operative sections; 2) predict argumentative relations between paragraphs. 📅 Important Dates 1 Feb: Train and test data release 18 March: Evaluation and submission starts 1 April: Submission ends 15 April: Evaluation ends; results notification 24 April: Paper submission due 1 May: Reviews to authors 12 May: Camera-ready version due July: ArgMining 2026 Workshop 🔗 Further details, data access, and submission instructions on the shared task page: https://shared-task-argmining.linguistik.uzh.ch/ Organizers: Yingqiang Gao, Anastassia Shaitarova, Reto Gubelmann, Patrick Montjouridès, Department of Computational Linguistics, University of Zurich (UZH) We welcome participation from researchers and practitioners across related areas of argument mining, LLM reasoning, information retrieval, and so on. We are looking forward to receiving your submission!

1 0

LREC 2026 Industry Day - Call for Abstracts
by Teresa Lynn 02 Feb '26

02 Feb '26

Dear colleagues, We are excited to extend our invitation for submission of *abstracts* for the Industry Day at LREC 2026. As many of you know, the Industry Day is designed to highlight real-world NLP applications, practices and lessons learned, offering a space for organisations to share practical insights, case studies, and perspectives that can help to bridge the gaps between academic research and industry. In particular, this year's Industry Day is also intended to act as a networking platform for conference participants, experts, and professionals to foster meaningful collaborations. We particularly welcome abstracts addressing topics such as: - Applied NLP systems and large-scale deployments - Enterprise use cases and industry challenges - Evaluation, scalability, and reliability in production environments - Responsible AI, governance, and compliance in NLP - Emerging trends and future directions from an industry perspective - Speech Resources and Processing - Less-Resourced/Endangered/Less-studied Languages Submitted abstracts should emphasise practical experience, impact, and insights rather than purely theoretical/empirical contributions. Submission details - Abstract length: 150-200 words - Submission deadline: February 16th 2026 - Notification of acceptance: March 13th 2026 - Presentation format: oral Accepted contributions will be featured as part of the Industry Day program and presented to a diverse audience of students, industry leaders and experts. Only an abstract submission is required for a presentation proposal (not a full paper) and the submissions differ from LREC main submissions in that they will *not be included* in the conference proceedings or ACL Anthology. We would be delighted to include your perspective and encourage you to share this invitation with relevant colleagues. For submission guidelines and a broader list of LREC 2026 topics, please visit: https://lrec2026.info/call-for-industry-day-talks/ Looking forward to seeing you in Mallorca in May! Teresa Lynn & Natalie Schluter Co-Chairs, LREC 2026 Industry Day

1 0

Fully funded PhD opportunity at Lancaster University
by Brezina, Vaclav 02 Feb '26

02 Feb '26

Dear all, We are delighted to offer a fully funded PhD studentship starting in October 2026, open to UK and international applicants. We welcome applications from outstanding candidates with experience in corpus linguistics, language testing, or quantitative applied linguistics, including skills in e.g. corpus design, statistical analysis, and tools such as R, Python or #LancsBox X. Delivered in collaboration with Trinity College London, the PhD will explore communicative competence in English language tests, with implications for fairness, accessibility and social mobility. The successful candidate will be based in Lancaster and work regularly on campus as part of the CASS team. Further details and application information are available here: https://cass.lancs.ac.uk/fully-funded-phd-opportunity-at-the-cass-research-… Please feel free to share this opportunity. Best, Vaclav Professor Vaclav Brezina Professor in Corpus Linguistics Co-Director of the ESRC Centre for Corpus Approaches to Social Science Faculty of Humanities, Arts and Social Sciences, Lancaster University Lancaster, LA1 4YD Office: County South, room B46 T: +44 (0)1524 510828 @vaclavbrezina

1 0

[CfP] TGDK Special Issue: Neuro-Symbolic Modeling for Human-Centric AI
by Marco Antonio Stranisci 02 Feb '26

02 Feb '26

================================================ Transactions on Graph Data & Knowledge (TGDK) https://www.dagstuhl.de/tgdk Special Issue: Neuro-Symbolic Modeling for Human-Centric AI https://www.dagstuhl.de/en/institute/news/2026/tgdk-cfp-special-issue-neuro… Submissions due: June 30th, 2026 ================================================ In recent years, the alignment of Artificial Intelligence technologies with people’s behaviors and worldviews has become a central topic for several sectors of Computer Science. The pervasive diffusion of Large Language Models (LLM) inside and outside the academic sector requires important efforts to ensure fairness and representativity towards all social and cultural groups, potentially considering different identities that characterize potential end-users of these technologies. This special issue welcomes contributions on the development of graph-based abstractions and implementations of graph-based approaches for human-centered AI. It welcomes hybrid neuro-symbolic and graph-based approaches focused on knowledge reasoning for learning, and learning approaches for reasoning, as well as the design and curation of graph-based data and semantic models to explore the inclusion and representation of human identities in AI systems. == Scope == This special issue solicits submissions of research, resource and survey articles that conform to the scope of TGDK on the following specific topics: Ontology modeling and knowledge representation for Human-Centric AI * Knowledge representation for reducing bias in AI * Ontologies of identity dimensions and psychology for AI * Ontologies of sociological and communication theories for AI * Linked Data approaches for Human-Centric AI Data quality, integration and provenance for Human-Centric AI * FAIR and CARE principles for AI models * Graph-based provenance approaches for AI models * Incorporating cultural metadata into AI workflows * KG-driven approaches for bias detection and mitigation in archives LLM integration with graph-structured knowledge for the design of fair AI technologies * Question answering with LLMs and graph-structured knowledge * Reducing LLM hallucinations with graph-structured knowledge * Injecting graph-structured knowledge into LLMs * Retrieval-Augmented Generation using graph-structured knowledge * Enhancing graph-structured knowledge using LLMs Logic and reasoning for Explainable AI * Logic-based methods for governance of AI * Logic-based methods for ethical AI frameworks * Logic-based methods for legal compliance of AI * Extraction of logic-based representations for explainable AI * Graph-based constraint languages for explainable AI == Guest Editors == * Stefano De Giorgis, Vrije Universiteit Amsterdam, Netherlands * Marco Antonio Stranisci, University of Turin, Italy * Luana Bulla, University of Bologna, Italy * Lia Draetta, University of Turin, Italy * Rossana Damiano, University of Turin, Italy * Filip Ilievski, Vrije Universiteit Amsterdam, Netherlands == Timeline == * Submissions: June 30, 2026 * Author Notifications: September 30, 2026 * Revisions: October 31, 2026 * Author Notifications: November 30, 2026 * Publication: Q4 2026 / Q1 2027 == Submission == Please follow the the submission instructions for TGDK and select the corresponding Special Issue: https://drops.dagstuhl.de/entities/journal/TGDK#author As a Diamond Open Access journal, official versions of accepted papers (as accessible via DOI) are published and made available for free online *without fees for authors or readers*. Marco, UNITO <https://www.unito.it/persone/mstranis> and aequa-tech <https://aequa-tech.com/> "Aoki è sboccato e ancora inesperto, ma dentro di sé nasconde una sensibilità delicata e gentile. È questo ciò che mi comunicano le sue storie. Hayashi, sono certo che lei riuscirà a illuminare il suo cammino" Taiyo Matsumoto

1 0

CFP: LT4HALA 2026
by Sprugnoli Rachele (rachele.sprugnoli) 02 Feb '26

02 Feb '26

CFP: LT4HALA 2026 - The Fourth Workshop on Language Technologies for Historical and Ancient Languages * Website: https://circse.github.io/LT4HALA/2026/ * Date: 11 May 2026 * Place: co-located with LREC 2026, 11-16 May 2026, Palma, Mallorca (Spain) * Submission page: https://softconf.com/lrec2026/LT4HALA2026/ * Submission deadline: 17 February 2026 DESCRIPTION LT4HALA 2026 is a one-day workshop that seeks to bring together scholars who are developing and/or are using Language Technologies (LTs) for historically attested languages, so to foster cross-fertilization between the Computational Linguistics community and the areas in the Humanities dealing with historical linguistic data, e.g. historians, philologists, linguists, archaeologists and literary scholars. LT4HALA 2026 follows LT4HALA 2020, 2022, 2024 that were organized in the context of LREC 2020, LREC 2022 and LREC-COLING 2024, respectively. Despite the current availability of large collections of digitized texts written in historical languages, such interdisciplinary collaboration is still hampered by the limited availability of annotated linguistic resources for most of the historical languages. Creating such resources is a challenge and an obligation for LTs, both to support historical linguistic research with the most updated technologies and to preserve those precious linguistic data that survived from past times. Relevant topics for the workshop include, but are not limited to: * creation and annotation of linguistic resources (both lexical and textual); * the role of digital infrastructures, such as CLARIN<https://www.clarin.eu/>, in supporting research based on language resources for historical and ancient languages; * handling spelling variation; * detection and correction of OCR errors; * deciphering; * morphological/syntactic/semantic analysis of textual data; * adaptation of tools to address diachronic/diatopic/diastratic variation in texts; * teaching ancient languages with LTs; * NLP-driven theoretical studies in historical linguistics; * NLP-driven analysis of literary ancient texts; * evaluation of LTs designed for historical and ancient languages; * LLMs for the automatic analysis of ancient texts. SHARED TASKS LT4HALA 2026 will host: * the 4th edition of EvaLatin<https://circse.github.io/LT4HALA/2026/EvaLatin>, a campaign entirely devoted to the evaluation of NLP tools for Latin. This new edition will focus on two tasks: dependency parsing and Named Entity Recognition. Dependency parsing will be based on the Universal Dependencies framework. * the 5th edition of EvaHan<https://circse.github.io/LT4HALA/2026/EvaHan>, the campaign for the evaluation of NLP tools for Ancient Chinese. EvaHan 2026 will focus on Ancient Chinese OCR (Optical Character Recognition) Evaluation. * the 2nd edition of EvaCun<https://circse.github.io/LT4HALA/2026/EvaCun>, the campaign for the evaluation of Ancient Cuneiform Languages, with shared tasks on transliteration normalization, morphological analysis and lemmatization, Named Entity Recognition of Akkadian and/or Sumerian. SUBMISSIONS Submissions should be 4 to 8 pages in length and follow the LREC stylesheet (see below). The maximum number of pages excludes potential Ethics Statements and discussion on Limitations, acknowledgements and references, as well as data and code availability statements. Appendices or supplementary material are not permitted during the initial submission phase, as papers should be self-contained and reviewable on their own. Papers must be of original, previously unpublished work. Papers must be anonymized to support double-blind reviewing. Submissions thus must not include authors’ names and affiliations. The submissions should also avoid links to non-anonymized repositories: the code should be either submitted as supplementary material in the final version of the paper, or as a link to an anonymized repository (e.g., Anonymous GitHub or Anonym Share). Papers that do not conform to these requirements will be rejected without review. Submissions should follow the LREC stylesheet, which is available on the LREC 2026 website on the Author’s kit page<https://lrec2026.info/authors-kit/>. Each paper will be reviewed by three independent reviewers. Accepted papers will appear in the workshop proceedings, which include both oral and poster papers in the same format. Determination of the presentation format (oral vs. poster) is based solely on an assessment of the optimal method of communication (more or less interactive), given the paper content. As for the shared tasks, participants will be required to submit a technical report for each task (with all the related sub-tasks) they took part in. Technical reports will be included in the proceedings as short papers: the maximum length is 4 pages (excluding references) and they should follow the LREC 2026 official format. Reports will receive a light review (we will check for the correctness of the format, the exactness of results and ranking, and overall exposition). All participants will have the possibility to present their results at the workshop. Reports of the shared tasks are not anonymous. WORKSHOP IMPORTANT DATES * 17 February 2026: submissions due * 13 March 2026: reviews due * 16 March 2026: notifications to authors * 27 March 2026: camera-ready due Shared tasks deadlines are available in the specific web pages: EvaLatin, EvaHan, EvaCun. Identify, Describe and Share your LRs! When submitting a paper from the START page, authors will be asked to provide essential information about resources (in a broad sense, i.e. also technologies, standards, evaluation kits, etc.) that have been used for the work described in the paper or are a new result of your research. Moreover, ELRA encourages all LREC authors to share the described LRs (data, tools, services, etc.) to enable their reuse and replicability of experiments (including evaluation ones). [http://static.unicatt.it/ext-portale/5xmille_firma_mail_2023.jpg] <https://www.unicatt.it/uc/5xmille>

1 0

Call for Participation: LT-EDI @ ACL 2026 - Homophobia and Transphobia Meme Classification
by Bharathi Raja Asoka Chakravarthi 01 Feb '26

01 Feb '26

*Homophobia and Transphobia Meme Classification | LT-EDI @ ACL 2026* We are pleased to invite the research community to participate in the LT-EDI @ ACL 2026 shared task on Homophobia and Transphobia Meme Classification, which addresses harmful multimodal content targeting LGBTQ+ individuals and communities. Memes function as compact multimodal communication units that combine visual and textual cues. They spread rapidly across cultures and languages. This combination enables both subtle and explicit forms of discrimination. The shared task focuses on the automatic identification of homophobic and transphobic content in memes. * 📝 Task Description*The shared task focuses on multiclass meme classification for detecting anti-LGBT content. Participants are provided with multimodal memes and are required to classify each meme into one of the predefined categories based on the presence of discriminatory content. *Labels:*Homophobia Transphobia Non-anti-LGBT *Languages:*English, Hindi, and Chinese *Description:*Separate datasets are released for each language, enabling analysis across culturally distinct meme collections. The task requires participants to identify discriminatory stereotypes, harmful visual elements, and derogatory textual cues embedded in memes. All training and test datasets are developed following culturally sensitive and ethical annotation practices. The task emphasizes robust multimodal understanding across diverse cultural contexts. * 📚 Resources* 🔗 Competition link (Codabench): https://www.codabench.org/competitions/11335/ 🔗 Task website: https://sites.google.com/view/lt-edi-2026/shared-tasks *🗓️ Important Dates*Task announcement: November 16, 2025 Training data release: November 25, 2025 Test data release: January 20, 2026 Run submission deadline: February 10, 2026 Results announcement: February 16, 2026 Paper submission deadline: March 5, 2026 Peer review notification: April 28, 2026 Camera-ready submission: May 12, 2026 Workshop dates: July 2–3, 2026 with regards, Dr. Bharathi Raja Chakravarthi, Assistant Professor / Lecturer-above-the-bar Programme Director (MSc Computer Science - Artificial Intelligence) <https://www.universityofgalway.ie/courses/taught-postgraduate-courses/compu…> School of Computer Science, University of Galway, Ireland Insight SFI Research Centre for Data Analytics, Data Science Institute, University of Galway, Ireland E-mail: bharathiraja.akr(a)gmail.com , bharathi.raja(a)universityofgalway.ie <bharathiraja.asokachakravarthi(a)universityofgalway.ie> Google Scholar: https://scholar.google.com/citations?user=irCl028AAAAJ&hl=en Website: https://research.universityofgalway.ie/en/persons/bharathi-raja-asoka-chakr…

1 0