July 2026 - Corpora - ELRA lists

Lectureship in English Language - University of Leeds
by Eric Atwell 19 Jul '26

19 Jul '26

University of Leeds UK Lectureship in English Language - AHCEN1087 Closing date: 31 Jul 2026 Grade: Grade 7 to Grade 8 - £41,064 to £59,966 per year https://jobs.leeds.ac.uk/AHCEN1087 We wish to appoint an outstanding teacher and scholar with a background in digital humanities-enabled English Language research. This might include but is not limited to: corpus linguistics and text analysis, computational linguistics, artificial intelligence, digital/interactive media, forensic linguistics, or sociolinguistic approaches. For further information, please contact: Professor Hazel Hutchison, Head of School Email: H.Hutchison(a)leeds.ac.uk <mailto:H.Hutchison@leeds.ac.uk%20>

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Recruitment of Research Annotator - AddressHate
by kglanz＠addresshate.org 17 Jul '26

17 Jul '26

Job Location: New York City, USA Web Address: https://www.addresshate.org/ Job Title: Research Annotator Job Rank: Other Specialty Areas: Computational Linguistics; General Linguistics; Lexicography; Sociolinguistics; Text/Corpus Linguistics Specialty Language(s): English (eng, stan1293) English based Description: Research Annotator AddressHate · Part-time · US-based (Eastern Time preferred; NYC-based ideal) About AddressHate: Hate speech online is growing faster than any organization can track by hand. AddressHate is building the tools to change that. We are a public interest technology company at the intersection of research, AI, and policy. We build detection infrastructure and intelligence tools that help platforms, policymakers, educators, civil society organizations, and real people understand and counter digital hate in real time. There is no good solution in the for-profit world for this problem, so we are building it. Our mission and the business are the same thing: the better we get at detecting hate, the more valuable our tools become to the world. The Opportunity: AddressHate is hiring Research Annotators to support our core mission: building detection infrastructure that identifies how hate circulates, adapts, and normalizes in digital environments. This is a direct hire position with AddressHate. All work product — including annotated datasets, analytical memos, and contributions to coding schemes — is owned by AddressHate. We’re looking for annotators with high sensitivity to the layered, implicit, and multimodal forms hate takes online — particularly where meaning is carried through irony, coding, visual reference, intertextual play, and shifting register across platforms. Rather than mechanical tagging, the position requires theory-informed interpretive work that feeds directly into AddressHate’s annotation pipeline and classifier development. Our work demonstrates that a large portion of hate content in mainstream digital spaces operates through implicit, coded, or multimodal forms — spread through irony, political framing, and shared references that are largely undetectable by automated systems. Understanding this requires human expertise grounded in linguistics, multimodal analysis, and discourse studies. The role is US-based (Eastern Time preferred; NYC-based ideal) and reports into AddressHate’s research and technology teams. Core Responsibilities: - Conduct continuous, systematic annotation of social media content across platforms including TikTok, X, Instagram, and Reddit - Analyze textual, visual, and multimodal content, including memes, image–text combinations, and visual rhetoric - Identify implicit meanings, indirect references, metaphors, narrative frames, and visual-symbolic cues - Identify shared rhetorical and discursive mechanisms across hate domains — including dehumanization, conspiracy narratives, victim–perpetrator reversal, moral inversion, and normalization strategies - Analyze intersecting and overlapping hate discourses, including cases where antisemitism, misogyny, and racism co-occur or mutually reinforce one another - Distinguish between legitimate political expression and harmful discourse, with close attention to context, pragmatics, and discursive positioning - Participate in regular team meetings and inter-annotator calibration sessions - Contribute to the iterative refinement of AddressHate’s coding schemes, category definitions, and annotation guidelines - Work closely with AddressHate’s data science team, translating qualitative insights into structured input for computational analysis - Produce analytical memos and structured documentation that make interpretive decisions transparent and reproducible What We’re Looking For: - MA or PhD student (or completed degree) in Linguistics, Discourse Studies, Semiotics, Visual Studies, Media Studies, Hate Studies, History, Political Science, or related field. Candidates with a background in linguistics and multimodal/visual analysis are particularly valued. - Demonstrated experience with qualitative content analysis, discourse-analytical methods, and mixed methods research - High awareness of the different layers of hate speech and digital hate — including implicit, coded, ambiguous, and context-dependent forms in both text and imagery - Native or near-native English proficiency with strong language sensitivity - Fluency with contemporary social media platforms, particularly TikTok, X, Instagram, and Reddit - Openness and resilience to engage with hate speech content, including antisemitism, different forms of racism, and misogyny - High motivation and capacity for autonomous work. The role involves sustained, continuous engagement with difficult online content. Strong candidates structure their time independently and maintain rigor and momentum across long annotation cycles. - Curiosity and flexibility. AddressHate’s annotation work is a collaborative environment where coding schemes evolve, new platforms enter the pipeline, and analytical questions sharpen over time. Strongly Preferred: - Research experience across more than one hate ideology — antisemitism, misogyny and gender-based hate, anti-Black racism, anti-Asian racism, or intersectional hate research. We particularly value candidates who can think comparatively across domains rather than specialists in only one. - Experience with multimodal analysis (memes, visual rhetoric, image–text relations) - Experience with MAXQDA or comparable qualitative analysis software - Strong written documentation skills - Based in or near New York City Why Now: By the time hate’s effects are visible in the world, the discourse that produced it has long since moved on. AddressHate is building the infrastructure to see it as it happens — and to understand what it means. The analytical tools we develop inform education, policy, and platform governance. This role sits at the center of that work. Terms: 20–30 hours per week · $25 USD per hour · Hired directly by AddressHate To Apply: Send your CV, a short statement of interest, and one writing sample (academic or analytical) to careers(a)addresshate.org Application Deadline: 31-Oct-2026 Mailing Address for Applications: 152 W 57th St 21st Floor, New York City, New York USA Email Address for Applications: careers(a)addresshate.org Web Address for Applications: https://www.addresshate.org/ Contact Information: Email: careers(a)addresshate.org

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Final Call for Papers – DDS 2026 at ISWC in Bari, Italy
by Pasquale Lisena 17 Jul '26

17 Jul '26

Dear colleagues, This is the final call for papers for the Data-driven Storytelling: Bridging Semantics, AI, and Narrative (DDS 2026) workshop, co-located with ISWC 2026 in Bari, Italy. We invite researchers and practitioners in knowledge graphs, NLP, HCI, and generative AI to submit their work exploring how semantic technologies can enhance narrative creation and engagement. Extended Submission Deadlines Abstracts: July 24, 2026 (23:59 AoE) Full papers: July 30, 2026 (23:59 AoE) Key Dates Notifications: August 21, 2026 Camera-ready Version: September 18, 2026 Workshop Dates: October 25–26, 2026 Topics of Interest We welcome submissions of research papers, demos, and short papers addressing: Knowledge graphs and ontologies for storytelling AI-driven narrative generation (LLMs, GenAI) Benchmarking narrative quality and coherence Interactive and participatory storytelling tools Ethics and explainability in automated storytelling Don’t miss the opportunity to share your work in Bari! Website and submission: https://data-driven-storytelling-workshop.replit.app/ We look forward to your contributions! Best regards, The DDS 2026 Organizing Committee -- Pasquale Lisena EURECOM, Campus SophiaTech 450 route des Chappes, 06410 Biot, France e-mail: pasquale.lisena(a)eurecom.fr site: http://pasqlisena.github.io/

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Third CfP: 13th Web-as-Corpus (WaC-13) Workshop @EMNLP2026, Budapest, Hungary, 29 Oct, 2026
by Veronika Laippala 17 Jul '26

17 Jul '26

Third Call for Papers 13th Web-as-Corpus (WaC-13) Workshop @EMNLP2026, Budapest, Hungary, 29 Oct, 2026 https://wackyworkshop.org The World Wide Web has evolved from a resource for building linguistic corpora into the central data infrastructure powering modern natural language processing and Large Language Models (LLMs). As web-scale data increasingly shapes AI systems’ knowledge and capabilities, understanding its quality, representativeness, and ethical implications has become critical. At the same time, the “more is better” paradigm is being challenged by issues such as machine-generated content, data toxicity, limited metadata, and the under-representation of many languages and domains. These challenges call for a shift toward Data-Centric AI, focusing on the curation, analysis, and responsible use of web-derived data. The 13th Web-as-Corpus (WaC-13) workshop provides a multidisciplinary forum for research addressing the full lifecycle of web data. We invite submissions on methods, resources, and applications related to web corpora, with special emphasis on multilingual data and less-resourced languages. Topics of interest include (but are not limited to): * Creation and evaluation of high-quality datasets for foundation models (e.g., data collection, filtering, enrichment, language identification) * Use of web data in empirical linguistic research * Analysis of web-scale corpora for quality, representativeness, and societal insights * Ethical and legal aspects of collecting, sharing, and using web data By bringing together researchers from NLP, linguistics, and the social sciences, WaC aims to advance best practices for one of the field’s most influential data sources. Important dates Direct paper submission deadline 7 August, 2026 Pre-reviewed ARR commitment deadline 1 September, 2026 Notification of acceptance 5 September, 2026 Camera-ready paper due 20 September, 2026 Workshop date 29 Oct, 2026 Submissions Submit your papers through https://openreview.net/group?id=EMNLP/2026/Workshop/WaC-13 (both archival and non-archival option available) or through ARR commitment https://openreview.net/group?id=EMNLP/2026/Workshop/WaC-13_ARR_Commitment. Workshop Organizers Nikola Ljubešić, Jožef Stefan Institute, Slovenia Yves Scherrer, University of Oslo, Norway Laurie Burchell, Common Crawl Foundation Veronika Laippala, University of Turku, Finland Pedro Ortiz Suarez, Common Crawl Foundation Thom Vaughan, Common Crawl Foundation Vuk Dinić, Jožef Stefan Institute, Slovenia

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[Second CFP] WASP 2026: 4th Workshop on Artificial Intelligence for Scientific Publications @AACL-IJCNLP 2026
by ACL Announcements 16 Jul '26

16 Jul '26

==================================================================== SECOND CALL FOR PAPERS The 4th Workshop on Artificial Intelligence for Scientific Publications (WASP 2026) Co-located with IJCNLP-AACL 2026 November 9, 2026, Online Website: https://ui.adsabs.harvard.edu/WIESP/2026/ [1] Submissions (OpenReview): https://openreview.net/group?id=aclweb.org/AACL-IJCNLP/2026/Workshop/WASP [2] ==================================================================== OVERVIEW The scientific literature now grows faster than anyone can read it. The claims, evidence, named entities, relations, and citations that researchers rely on remain in unstructured text rather than in any form a machine can use directly. Turning that text into structured, machine-readable knowledge is a central problem in NLP, and many of the questions it raises remain open. WASP 2026 brings together researchers working to extract, structure, and mine knowledge from scholarly publications. As large language models reshape how scientific text is parsed, mined, and synthesized, this iteration keeps information extraction at its center while broadening to the full pipeline that turns publications into usable knowledge, from summarization and retrieval to evaluation and the responsible use of LLMs in scientific publishing. WASP continues a series that began as the Workshop on Information Extraction from Scientific Publications (WIESP) at AACL-IJCNLP 2022 and IJCNLP-AACL 2023, and grew into WASP at IJCNLP-AACL 2025. TOPICS OF INTEREST (not limited to) - Scientific document parsing and structured information extraction; citation context/span extraction and citation-based knowledge mining - Scientific named-entity recognition and concept identification - Scientific literature quality assessment and automated peer review assistance - Argument extraction and scientific discourse analysis - Scientific article summarisation and headline generation - Question-answering and fact retrieval from scientific literature - Pretraining and fine-tuning LLMs on scientific corpora - Evaluation and alignment of LLMs for scientific understanding - AI-assisted scientific discovery and hypothesis generation - Cross-lingual scientific knowledge transfer and multilingual publication processing - Scientific trend analysis and research gap identification - Reproducibility and replicability analysis in scientific publications - Ethical and responsible use of LLMs in scientific publishing - Automated metadata generation and semantic tagging of scientific publications - Scientific plagiarism detection and research integrity monitoring - Scientific collaboration network analysis and co-authorship prediction - Automated scientific writing assistance and manuscript generation - Scientific image and figure analysis in publications SUBMISSION Long papers: up to 8 pages of content (9 in the camera-ready), plus unlimited references. Short papers: up to 4 pages of content (5 in the camera-ready), plus unlimited references. All submissions are double-blind and must follow the host venue formatting guidelines and ACL template files. We follow AACL-IJCNLP 2026 policies on anonymity, preprints, and double submissions. Accepted papers will appear in the WASP 2026 proceedings in the ACL Anthology. Submit via OpenReview: https://openreview.net/group?id=aclweb.org/AACL-IJCNLP/2026/Workshop/WASP [2] SHARED TASK WASP 2026 will host a shared task. Details and a separate call for papers will be announced soon. System description papers will undergo light peer review and appear in the proceedings. IMPORTANT DATES (Anywhere on Earth) * Paper submission deadline (WASP): September 14, 2026 * Registration deadline for shared task: [TBA] * System Run and Output Submission (shared task): [TBA] * System Paper Submission (shared task): [TBA] * Notification of acceptance (WASP + Shared Task): October 1, 2026 * Camera-ready deadline (WASP + Shared Task): October 12, 2026 * Workshop: November 9, 2026 ORGANIZERS - Atilla Kaan Alkan, Center for Astrophysics | Harvard & Smithsonian, USA - Alberto Accomazzi, Center for Astrophysics | Harvard & Smithsonian, USA - Tirthankar Ghosal, Oak Ridge National Laboratory, USA - Felix Grezes, Center for Astrophysics | Harvard & Smithsonian, USA - Kelly Lockhart, Center for Astrophysics | Harvard & Smithsonian, USA CONTACT For inquiries: Atilla Kaan Alkan, atilla.alkan(a)cfa.harvard.edu We look forward to your contributions. Links: ------ [1] https://ui.adsabs.harvard.edu/WIESP/2026/ [2] https://openreview.net/group?id=aclweb.org/AACL-IJCNLP/2026/Workshop/WASP

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[CFP:Survey] Survey on Knowledge Graph enrichment
by Oğuzhan MENEMENCİOĞLU 16 Jul '26

16 Jul '26

Apologies for cross-posting. Dear Colleague, We kindly invite you to share your experience on knowledge graph enrichment by completing this survey: <https://forms.gle/tyW9M42nQc8fciLV6> https://forms.gle/tyW9M42nQc8fciLV6 We are especially interested in the meaning and boundaries of the KG enrichment task, best practices and pitfalls, and suggested methods for KG enrichment. Form completion can take 5 to 10 minutes. Your insights will contribute to a public deliverable on best practices and practical guidelines for KG enrichment. We appreciate your time and input. Please feel free to forward this invitation to your colleagues. Best regards, the Chairs of <https://goblin-cost.eu/working-group-1-knowledge-graphs-engineering/> GOBLIN Task 1.4<https://goblin-cost.eu/working-group-1-knowledge-graphs-engineering/> Dr. Oğuzhan MENEMENCİOĞLU ---------------------------------------------------------------------------------- http://oguzhan.menemencioglu.info<http://oguzhan.menemencioglu.info/> Assistant Professor Karabük University, Department of Computer Engineering, Office 159, Iron and Steel Campus, 78050, Karabük, Turkey.

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July 2026 Newsletter - LDC
by Penn LDC 15 Jul '26

15 Jul '26

In this newsletter: Fall 2026 LDC data scholarship program New publications: 2012 NIST Speaker Recognition Evaluation Test Set<https://catalog.ldc.upenn.edu/LDC2026S09> CALLHOME American English Second Edition<https://catalog.ldc.upenn.edu/LDC2026S08> CALLHOME American English Lexicon (PRONLEX) Second Edition<https://catalog.ldc.upenn.edu/LDC2026L05> ________________________________ Fall 2026 LDC data scholarship program Student applications for the Fall 2026 LDC data scholarship program are being accepted now through September 15, 2026. This program provides eligible students with no-cost access to LDC data. Students must complete an application consisting of a data use proposal and letter of support from their advisor. For application requirements and program rules, visit the LDC Data Scholarships<https://www.ldc.upenn.edu/language-resources/data/data-scholarships> page. ________________________________ New publications: 2012 NIST Speaker Recognition Evaluation Test Set<https://catalog.ldc.upenn.edu/LDC2026S09> was developed by LDC and NIST and contains 10,321 hours of English conversational telephone speech and in-person recorded studio sessions for evaluation and modeling, along with answer keys, trial files, and documentation from the NIST-sponsored 2012 Speaker Recognition Evaluation (SRE12)<https://www.nist.gov/itl/iad/mltg/speaker-recognition-evaluation-2012>. SRE12 introduced a revised evaluation structure in which training data for target speakers was drawn from prior SRE corpora developed by LDC and was provided in advance of the evaluation period. Test data was drawn from Mixer 7 English Speech (LDC2025S08)<https://catalog.ldc.upenn.edu/LDC2025S08> and REMIX Telephone Collection (LDC2023S09)<https://catalog.ldc.upenn.edu/LDC2023S09>. Those datasets also provided segments for modeling data; other modeling segments were drawn from Mixer 3 Speech (LDC2023S02)<https://catalog.ldc.upenn.edu/LDC2023S02>, Mixer 4 and 5 Speech (LDC2020S03)<https://catalog.ldc.upenn.edu/LDC2020S03>, and Mixer 6 Speech (LDC2013S03)<https://catalog.ldc.upenn.edu/LDC2013S03>. The test data contains English speech only; some non-English speech is contained in modeling segments. This release is comprised of 130,844 test segments, specifically, 83,778 call segments and 47,066 interview segments. Modeling data consists of 46,948 segments. 2026 members can access this corpus through their LDC accounts. Non-members may license this data for a fee. * CALLHOME American English Second Edition<https://catalog.ldc.upenn.edu/LDC2026S08> was developed by LDC and contains 56 hours of speech from 120 unscripted telephone conversations between native American English speakers. This publication is a re-release of the original CALLHOME American English collection, combining CALLHOME American English Speech (LDC97S42)<https://catalog.ldc.upenn.edu/LDC97S42> and CALLHOME American English Transcripts (LDC97T14)<https://catalog.ldc.upenn.edu/LDC97T14>, with additional transcription and updated directory structure, file formats, and documentation. This release contains the 120 telephone conversations published in CALLHOME American English Speech which represented training and development data and a subset of evaluation data. Participants spoke on topics of their choice in a single telephone call lasting up to 30 minutes. Calls were manually audited for gender, language, recording quality, channel characteristics, dialect, and region. For this second edition, all audio was converted from SPHERE files to FLAC format, and the original training/development/evaluation partitioning was removed. This release also features revised transcripts conforming to updated LDC transcription guidelines that addressed normalization of annotation formats, standardization of speaker-produced and background noises, application of foreign-language marking, whitespace cleanup, and corrections and consistency fixes. The CALLHOME series consists of telephone conversations and transcripts developed by LDC and Rutgers, The State University of New Jersey, in support of research in speaker identification, language identification, and related technologies. Languages in the series include American English, Egyptian Arabic, German, Japanese, Mandarin Chinese, and Spanish. 2026 members can access this corpus through their LDC accounts. Non-members may license this data for a fee. * CALLHOME American English Lexicon (PRONLEX) Second Edition<https://catalog.ldc.upenn.edu/LDC2026L05> was developed by LDC and contains 90,988 English words with citation-form pronunciations. This second edition updates file formats, directory structure, and documentation. The first edition is available as CALLHOME American English Lexicon (PRONLEX) (LDC97L20)<https://catalog.ldc.upenn.edu/LDC97L20>. The words in the lexicon were derived from Wall Street Journal text used in the continuous speech recognition publication series CSR-1 WSJ0 Complete (LDC93S6A<https://catalog.ldc.upenn.edu/LDC93S6A>), transcripts<https://isip.piconepress.com/projects/switchboard/> from the Switchboard telephone collection (LDC97S62)<https://catalog.ldc.upenn.edu/LDC97S62>, and transcripts representing unscripted telephone conversations between native American English speakers contained in CALLHOME American English Second Edition (LDC2026S08)<https://catalog.ldc.upenn.edu/LDC2026S08>. PRONLEX transcription is a phonemic transcription system designed to support speech recognition by providing a consistent and simplified representation of how words are pronounced in standard American English that allows variation to be generated later to avoid listing many pronunciation variations for each word. This single systematic base form can be expanded through rules or modeling. The transcription was created using a modified ARPABET phoneme set<https://learnius.com/slp/3+Speech+Production%2C+Perception+and+Phonetics/4+…>. The lexicon contains three tab-separated information fields: (1) word: orthographic representation of word; (2) pron: transcribed citation-form pronunciations using modified ARPABET phoneme set; and (3) comments: (OPTIONAL) comment on the entry. It is presented as a tab-delimited TSV file encoded in UTF-8 format and includes a pronunciation dictionary derived from the lexicon in UTF-8 encoded CMUdict<https://stdlib.io/docs/api/latest/@stdlib/datasets/cmudict> format. 2026 members can access this corpus through their LDC accounts provided they have submitted a completed copy of the special license agreement. Non-members may license this data for a fee. To unsubscribe from this newsletter, log in to your LDC account<https://catalog.ldc.upenn.edu/login> and uncheck the box next to "Receive Newsletter" under Account Options or contact LDC for assistance. Membership Coordinator Linguistic Data Consortium<ldc.upenn.edu> University of Pennsylvania T: +1-215-573-1275 E: ldc(a)ldc.upenn.edu<mailto:ldc@ldc.upenn.edu> M: 3600 Market St. Suite 810 Philadelphia, PA 19104

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[CfP] Deadline Reminder - NL4AI @ AIxIA 2026 - 9th Workshop on Natural Language for Artificial Intelligence | 6-9 October 2026, Perugia, Italy
by Elisa Leonardelli 15 Jul '26

15 Jul '26

[Apologies for Cross Posting] ═══════════════════════════════════════════════════════════ NL4AI 2026 – 9th Workshop on Natural Language for Artificial Intelligence, at the 24th International Conference of the Italian Association for Artificial Intelligence (AIxIA 2026) 6–9 October 2026 | Perugia, Italy ═══════════════════════════════════════════════════════════ Website: http://sag.art.uniroma2.it/NL4AI/ Contact Email: nl4ai2026(a)gmail.com ── IMPORTANT DATES ────────────────── [EXTENDED] Paper Submission deadline: 24 July 2026, 23:59 AoE (Firm Deadline)🚨 Notification to authors: 19 August 2026 Camera-ready due: 26 August 2026 Workshop Dates: 6–9 October 2026 ──────────────────────────────────── We invite submissions to NL4AI 2026, the Ninth Workshop on Natural Language for Artificial Intelligence, to be held in Perugia from the 6th to the 9th of October 2026, within the 24th International Conference of the Italian Association for Artificial Intelligence (AIxIA 2026), and supported by AILC (http://www.ai-lc.it/). The goal of NL4AI is to explore the role of Computational Linguistics and Natural Language Processing in Artificial Intelligence applications. We believe that new technological challenges and opportunities arise at the boundary between NLP and AI. On the one hand, AI applications benefit from a deeper understanding of problems related to Natural Language, and thus the integration of advanced NLP techniques. On the other hand, NLP benefits greatly from being used in wider areas of AI where problems and methodologies related to NL can be evaluated in new contexts. TOPICS OF INTEREST - Topics include but are not limited to: - NLP and AI Applications (health, legal domain, social media and journalism, etc.) - Natural Language Interfaces for Human Robot Interaction - Resources, Benchmarks, and Evaluation - Discourse and Pragmatics - Semantics - Natural Language Generation - Creativity, Style, and Narrative Generation - Summarization - Information Extraction in AI Applications - Machine Learning for NLP - LLMs, Foundation Models and Applications - Interpretability, Explainability and Analysis of Models for NLP - Natural Language Inference - Question Answering and Reading Comprehension - Sentiment Analysis, Opinion Mining, and Argumentation - Abusive Language Detection and Analysis - NLP for Fact Checking, Fake News Detection and Analysis - Conversational Agents in Human-Computer Interaction - Speech and Spoken Language Processing - Language and other Multimodality - Multimodal (text-image) data sources - Machine Translation and Multilinguality - Low-Resource NLP and Linguistic Diversity - Cognitive Modeling and Psycholinguistics - Computational Historical Linguistics, Social Science, and Cultural Analytics - Ethics, Fairness, and Societal Impacts of NLP - NLP and Industrial Challenges - Accepted papers will be published in the workshop proceedings via CEUR Workshop Proceedings. Depending on the number and quality of papers received, we will consider proposing a special issue in relevant journals. The Program Committee will select the Best Workshop Paper from the accepted papers. SUBMISSIONS We encourage original submissions that describe new theoretical models, applied techniques, and research in progress. Substantial extensions to works already published or presented in other locations are welcome as well. We invite two kinds of submissions: - Short/Demo Paper. Maximum length of 6 pages + up to 2 pages of Acknowledgements/Declaration on Generative AI/References - Regular Papers. Maximum length of 12 pages + up to 2 pages of references Acknowledgements/Declaration on Generative AI/References. Please note that papers with less than 25000 characters will be considered short papers in the CEUR proceedings. Submissions Evaluation. Submissions will be peer-reviewed (single-blind) by the program committee members. Evaluation criteria will include novelty, significance for theory/practice, technical soundness, and quality of presentation. Note that reviewers will not be required to evaluate appendices providing a review of the papers. Appendices are intended for including details for reproducibility and/or additional results. How to Submit. Proceedings shall be submitted to CEUR-WS.org <http://ceur-ws.org/> for online publication and all papers must follow the 2022 CEUR-ART - 1 Column paper style. The LaTeX template can be downloaded as source file from the NL4AI website <http://sag.art.uniroma2.it/NL4AI/wp-content/uploads/2026/05/CEURART-NL4AI-2…> or accessed as a Template in Overleaf <https://it.overleaf.com/read/cfvwgnqpvpys%23a7a014>. All the papers should be submitted via EasyChair: https://easychair.org/conferences/?conf=nl4ai2026 Note: All submissions must be compatible with CEUR (https://ceur-ws.org/) and include the CEUR Declaration on Generative AI section ( https://ceur-ws.org/GenAI/Policy.html). Papers missing this section will be desk rejected. WORKSHOP ORGANIZERS Alessandro Bondielli (University of Pisa) Giovanni Bonetta (Fondazione Bruno Kessler) Elisa Leonardelli (Fondazione Bruno Kessler) Irene Siragusa (University of Palermo) We look forward to seeing you in Perugia! The NL4AI 2026 Workshop Organizers -- -- Le informazioni contenute nella presente comunicazione sono di natura privata e come tali sono da considerarsi riservate ed indirizzate esclusivamente ai destinatari indicati e per le finalità strettamente legate al relativo contenuto. Se avete ricevuto questo messaggio per errore, vi preghiamo di eliminarlo e di inviare una comunicazione all’indirizzo e-mail del mittente. -- The information transmitted is intended only for the person or entity to which it is addressed and may contain confidential and/or privileged material. If you received this in error, please contact the sender and delete the material.

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🚀 2nd Workshop on Smarter Extraction of ScholArly MEtadata using Knowledge Graphs, Language Models and Agents (SESAME) at JCDL 2026
by Ansgar Scherp 14 Jul '26

14 Jul '26

📍 /Co-located with JCDL 2026, October 13-26, 2026, Texas, USA/ 🌐 Website: https://sesame-workshop.github.io/SESAME/ *Topics of interest include: * * 📚 Research Artifacts Metadata Modeling and Granularity * 🤖 Large Language Models (LLMs), NLP, and *Agents* for Metadata * 🕸️ Knowledge Graphs and Linked Data * 🏛️ Digital Libraries and Infrastructure * 🌍 Societal, Ethical Impact, and Future Policy Directions We invite original research contributions addressing the above topics in three categories: * *Long Papers:* 6–8 pages (excluding references) * *Short or Position Papers:* 2–4 pages (excluding references) * *Demo, Dataset, or Benchmark Papers*: 2–4 pages (excluding references) 📅 *Important Dates* * 📝 Paper submission: 15.08.2026 * 📩 Author notification: 01.09.2026 * 📄 Camera-ready submission: 10.09.2026 * 📍 Workshop date: *To be announced*

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Call for papers: Workshop on Collective States in Multimodal Interaction, at ICMI 2026
by Gualtiero Volpe 13 Jul '26

13 Jul '26

Apologies for cross-posting. Dear colleagues, We invite you to our workshop: Collective States in Multimodal Interaction 9 October 2026 at ICMI 2026, Napoli, Italy https://honda-research-institute.github.io/Collective_States_in_Multimodal_… Call for Papers We invite you to submit your unpublished work to the Workshop on Collective States in Multimodal Interaction, to be held in conjunction with the 28th ACM International Conference on Multimodal Interaction (ICMI 2026) in Napoli, Italy. The workshop will take place on one of the conference workshop days, 9 October 2026. Accepted papers will be presented at the workshop. See the workshop website for updates and submission details. Understanding collective states in group interaction is an emerging challenge at the intersection of multimodal AI, communication science, and social signal processing. While a large body of prior work has focused on individuals or dyads, many real-world settings, from meetings and brainstorming sessions to collaborative human-AI teams, depend on group-level phenomena such as collective engagement, shared attention, group affects, rapport, cohesion, and coordination. Recent progress in multimodal sensing and agentic AI creates new opportunities to model how these collective states arise, evolve, and influence individual behavior within groups. This workshop focuses on multi-party interaction settings in which AI systems must not only sense individuals, but also understand emergent group dynamics and, potentially, support them as peers or facilitators. The goal is to bring together researchers across artificial intelligence, multimodal interaction, and organization and communication science to advance methods, benchmarks, and applications for sensing and reasoning about collective states. The workshop will feature invited talks by Prof. Carlos Busso (Carnegie Mellon University) and Prof. Kazuhiro Otsuka (Yokohama National University). Topics of interest include, but are not limited to: * Multimodal sensing and modeling of collective states in group interaction * Group-level phenomena such as collective engagement, shared attention, group affect, rapport, cohesion, and coordination * Links between individual participant states and emergent group dynamics * Audio, video, language, physiological, and behavioral cues for multiparty interaction analysis * Multimodal fusion and temporal modeling for group behavior understanding * Annotation schemes, datasets, and benchmarks for collective states * AI agents as peers or facilitators in human-AI hybrid teams * Adaptive systems for improving participation, coordination, brainstorming, and collaboration * Explainability, robustness, fairness, privacy, and ethics in group sensing * Applications in meetings, education, healthcare, robotics, organizational settings, and other collaborative environments Submission Information We invite authors to submit papers in accordance with the ICMI formats via the PCS. Submissions will be peer-reviewed by the workshop committee. The review process will be double-blind. Important Dates Paper submission deadline: 17 July 2026 Notification to authors: 27 July 2026 Camera-ready deadline: 2 August 2026 Workshop day: 9 October 2026 Best, Giovanna Varni

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Corpora July 2026