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Postdoc position on LLM post-training at the University of Oslo
by Erik Velldal 03 Feb '26

03 Feb '26

[Apologies for cross-posting] A 3-year postdoctoral fellowship in Natural Language Processing (NLP) is available in the Language Technology Group (LTG) at the University of Oslo (UiO), Norway. The position is also affiliated with the Integreat Centre of Excellence and will primarily focus on preference learning for Large Language Models (LLMs), particularly in data-constrained settings. For more information about the position and the research environment, please see the full announcement here: https://www.jobbnorge.no/en/available-jobs/job/294378/postdoctoral-fellow-i… Closing date: 4th March 2026 Best regards, -erik -- Erik Velldal Language Technology Group Section for Machine Learning Department of Informatics, University of Oslo

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CMC-Corpora 2026 submissions now open
by Steven Coats 02 Feb '26

02 Feb '26

Dear colleagues, (Apologies for cross-posting) Submissions are now open for the 13th International Conference on CMC and Social Media Corpora for the Humanities (CMC-Corpora 2026), to be held 27–28 August 2026 at the University of Oulu, Finland. We invite submissions on computer-mediated communication and social media corpora, including both short papers (oral presentations) and poster/demo abstracts. Topics include corpus creation and annotation, analysis of CMC and social media communication, and computational/NLP approaches to digital discourse, including multimodal and AI-based methods. Submission deadline: 15 April 2026, 23:59 EEST Submission system: https://conftool.net/cmc2026 Conference website & templates: https://cmc2026.org<https://cmc2026.org/> All submissions will undergo double-blind peer review. Accepted short papers will be published in the conference proceedings, and selected papers may be invited for an extended publication after the conference. We would be grateful if you could circulate this announcement to interested colleagues and mailing lists. Best regards, Steven Coats (on behalf of the CMC-Corpora 2026 Organizing Committee) cmc2026(a)oulu.fi<mailto:cmc2026@oulu.fi> University Lecturer, Docent English, Faculty of Humanities University of Oulu P.O. Box 8000, FI-90014 University of Oulu Finland https://cc.oulu.fi/~scoats

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Special Thematic Session "𝗚𝗲𝗻𝗔𝗜 𝗳𝗼𝗿 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗔𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆: 𝗣𝗹𝗮𝗶𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗮𝗻𝗱 𝗜𝗻𝗰𝗹𝘂𝘀𝗶𝘃𝗲 𝗨𝘀𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻" at ICCHP 2026
by PALOMA MARTINEZ FERNANDEZ 02 Feb '26

02 Feb '26

📢 Call for Papers – Special Thematic Session (STS) at ICCHP 2026 International Conference on Computers Helping People with Special Needs 2026 July 13 - 17, 2026 Masaryk University Brno, Czech Republic We are pleased to announce our Special Thematic Session "𝗚𝗲𝗻𝗔𝗜 𝗳𝗼𝗿 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗔𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆: 𝗣𝗹𝗮𝗶𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗮𝗻𝗱 𝗜𝗻𝗰𝗹𝘂𝘀𝗶𝘃𝗲 𝗨𝘀𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻" <https://icchp.org/session/1080/> at ICCHP 2026 <https://icchp.org/>. Generative AI (GenAI) is rapidly reshaping how people interact with technology. While its inclusive potential is often highlighted, cognitive accessibility remains largely underexplored. This STS aims to bring together researchers and practitioners working at the intersection of Generative AI, Human–Computer Interaction, and cognitive accessibility. 🔎 Topics of interest include (but are not limited to): - Accessible, usable, and understandable Generative AI interaction for people with cognitive impairments - Plain Language and Easy-to-Read content generation, adaptation, and evaluation using Large Language Models (LLMs) - Human-in-the-loop approaches, participatory design, and co-creation methods for GenAI accessibility - Intelligent User Interfaces (IUI) for cognitive accessibility, including adaptive interfaces, personalization, and assistive interaction - Multimodal GenAI for cognitive accessibility: text–image–audio interaction, pictograms, visual supports, and alternative representations - Cognitive load reduction in GenAI systems: UX patterns, explainability, transparency, trust, and user control - Accessible and inclusive communication, including user-oriented language and caregiver support - Evaluation protocols, datasets, and methodologies with target users, including qualitative, quantitative, and mixed-methods approaches - Ethics, risk assessment, bias, and safeguards in GenAI systems for cognitive accessibility and vulnerable populations - Datasets, benchmarks, and metrics for evaluating readability, fluency, user preferences, and multilingual considerations 🗓️ Important dates (https://icchp.org/submission-26/ <https://lnkd.in/eiGPJg6e>): • Extended Abstract (3–5 pages): February 9, 2026 • Notification of Acceptance: March 23, 2026 • Final Camera-Ready Paper (6–8 pages, LNCS format): April 16, 2026 We warmly invite research papers, case studies, design proposals, and empirical work. + info: https://icchp.org/session/1080/ Paloma Martínez Full professor Human Language and Accessibility Technologies Group (hulat.inf.uc3m.es <http://labda.inf.uc3m.es>) Computer Science Department Universidad Carlos III de Madrid @Grupo_HULAT

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3rd CFP: Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026
by Paul Thompson 02 Feb '26

02 Feb '26

Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026 https://bionlp.nlm.nih.gov/cl4health2026/ LREC 2026 Palma, Mallorca (Spain) SCOPE CL4Health fills the gap among the different biomedical language processing workshops by providing a general venue for a broad spectrum of patient-oriented language processing research. The third workshop on patient-oriented language processing follows the successful CL4Health workshops (co-located with LREC-COLING 2024 and NAACL 2025), which clearly demonstrated the need for a computational linguistics venue focused on language related to public health. CL4Health is concerned with the resources, computational approaches, and behavioral and socio-economic aspects of the public interactions with digital resources in search of health-related information that satisfies their information needs and guides their actions. The workshop invites papers concerning all areas of language processing focused on patients' health and health-related issues concerning the public. The issues include, but are not limited to, accessibility and trustworthiness of health information provided to the public; explainable and evidence-supported answers to consumer-health questions; accurate summarization of patients' health records at their health literacy level; understanding patients' non-informational needs through their language, and accurate and accessible interpretations of biomedical research. The topics of interest for the workshop include, but are not limited to the following: * Health-related information needs and online behaviors of the public; * Quality assurance and ethics considerations in language technologies and approaches applied to text and other modalities for public consumption; * Summarization of data from electronic health records for patients; * Detection of misinformation in consumer health-related resources and mitigation of potential harms; * Consumer health question answering (Community Question Answering)(CQA); * Biomedical text simplification/adaptation; * Dialogue systems to support patients' interactions with clinicians, healthcare systems, and online resources; * Linguistic resources, data, and tools for language technologies focusing on consumer health; * Infrastructures and pre-trained language models for consumer health; IMPORTANT DATES February 18, 2026 -Workshop Paper Due Date️ March 13, 2026 - Notification of acceptance March 20, 2026 - Camera-ready papers due April 10, 2026 - Pre-recorded video due (hard deadline) May 12, 2026 - Workshop SHARED TASKS Detecting Dosing Errors from Clinical Trials (CT-DEB'26). Clinical Trials Dosing Errors Benchmark 2026 is a challenge to predict medication errors in clinical trials using Machine Learning. The Clinical Trials Dosing Errors Benchmark 2026 (CT-DEB'26) is dedicated to automated detection of the risks of medication dosing errors within clinical trial protocols. Leveraging a curated dataset of over 29K trial records derived from the ClinicalTrials.gov<http://clinicaltrials.gov/> registry, participants are challenged to predict the risk probabilities of protocols likely to manifest dosing errors. The dataset consists of various fields with numerical, categorical, as well as textual data types. Once the shared task is concluded and the leaderboard is published, the participants are invited to submit a paper to the CL4Health workshop. Website: https://www.codabench.org/competitions/11891/ Automatic Case Report Form (CRF) Filling from Clinical Notes. Case Report Forms (CRFs) are standardized instruments in medical research used to collect patient data in a consistent and reliable way. They consist of a predefined list of items to be filled with patient information. Each item aims to collect a portion of information relevant for a specific clinical goal (e.g., allergies, chronicity of disease, tests results). Automating CRF filling from clinical notes would accelerate clinical research, reduce manual burden on healthcare professionals, and create structured representations that can be directly leveraged to produce accessible, patient- and practitioners-friendly summaries. Even though the healthcare community has been utilizing CRFs as a basic tool in the day-to-day clinical practice, publicly available CRF datasets are scarce, limiting the development of robust NLP systems for this task. We present this Shared Task on CRF-filling aiming to enhance research on systems that can be applied in real clinical settings. Website: https://sites.google.com/fbk.eu/crf/ ArchEHR-QA 2026: Grounded Question Answering from Electronic Health Records. The ArchEHR-QA (“Archer”) shared task focuses on answering patients’ health-related questions using their own electronic health records (EHRs). While prior work has explored general health question answering, far less attention has been paid to leveraging patient-specific records and to grounding model outputs in explicit clinical evidence, i.e., linking answers to specific supporting content in the clinical notes. The shared task dataset consists of patient-authored questions, corresponding clinician-interpreted counterparts, clinical note excerpts with sentence-level relevance annotations, and reference clinician-authored answers grounded in the notes. ArchEHR-QA targets the problem of producing answers to patient questions that are supported by and explicitly linked to the underlying clinical notes. This second iteration builds on the 2025 challenge (which was co-located with the ACL 2025 BioNLP Workshop) by expanding the dataset and introducing four complementary subtasks spanning question interpretation, clinical evidence identification, answer generation, and answer–evidence alignment. Teams may participate in any subset of subtasks and will be invited to submit system description papers detailing their approaches and results. Website: https://archehr-qa.github.io/ FoodBench-QA 2026: Grounded Food & Nutrition Question Answering. FoodBench-QA 2026 is a shared task challenging systems to answer food and nutrition questions using evidence from nutrient databases and food ontologies.The dataset includes realistic dietary queries, ingredient and their quantities lists, and recipe descriptions, requiring models to perform nutrient estimation, FSA traffic-light prediction, and food entity recognition/linking across three food semantic models. Participants must generate accurate, evidence-based answers across these subtasks (or at least one of it). After the shared task concludes and the leaderboard is released, participants will be invited to submit their work to the Shared Tasks track of the CL4Health workshop at LREC 2026. Website: https://www.codabench.org/competitions/12112/ SUBMISSIONS Two types of submissions are invited: - Full papers: should not exceed eight (8) pages of text, plus unlimited references. These are intended to be reports of original research. - Short papers: may consist of up to four (4) pages of content, plus unlimited references. Appropriate short paper topics include preliminary results, application notes, descriptions of work in progress, etc. Electronic Submission: Submissions must be electronic and in PDF format, using the Softconf START conference management system. Submissions need to be anonymous. Papers should follow LREC 2026 formatting. LREC provides style files for LaTeX and Microsoft Word at https://lrec2026.info/authors-kit/. Submission site: https://softconf.com/lrec2026/CL4Health/ Dual submission policy: papers may NOT be submitted to the workshop if they are or will be concurrently submitted to another meeting or publication. 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). MEETING The workshop will be hybrid. Virtual attendees must be registered for the workshop to access the online environment. Accepted papers will be presented as posters or oral presentations based on the reviewers’ recommendations. ORGANIZERS - Deepak Gupta, US National Library of Medicine - Paul Thompson, National Centre for Text Mining and University of Manchester, UK - Dina Demner-Fushman, US National Library of Medicine - Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK -- Paul Thompson Research Fellow Department of Computer Science National Centre for Text Mining Manchester Institute of Biotechnology University of Manchester 131 Princess Street Manchester M1 7DN UK http://personalpages.manchester.ac.uk/staff/Paul.Thompson/

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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>.

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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

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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

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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

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📢 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!

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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

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