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@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