- Corpora - ELRA lists

Recrutement 1 PR et 1 MCF : Informatique, LISN, Université Paris Saclay
by Agata Savary 13 Feb '26

13 Feb '26

ENGLISH VERSION FOLLOWS Bonjour, Le LISN et l'Université Paris-Saclay recrutent : - un maître ou une maitresse de conférence en Informatique (section 27) - un professeur des universités ou une professoresse des universités en Informatique (section 27) Les personnes recrutées seront en poste au sein de l'UFR des Sciences de la Faculté d'Orsay (département informatique). Le LISN est un laboratoire interdisciplinaire de recherche en informatique et sciences du numérique avec une expertise en intelligence artificielle reconnue au niveau international. Les thèmes qui seront considérés en priorité sont les suivants (ordre alphabétique) : Apprentissage statistique Bioinformatique IA hybride, neuro-symbolique, représentation des connaissances et raisonnement Numérique responsable Traitement automatique des langues Les personnes candidates sont invitées à consulter la politique de recrutement pluriannuelle en IA du LISN: https://www.lisn.upsaclay.fr/recherche/lia-au-lisn/ et à prendre contact avant le 31 mars 2026 avec les responsables des équipes et/ou départements de recherche dans lesquels leur projet de recherche pourra s'inscrire. * département Science des données (SD) <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/science-des-…>: Guillaume Charpiat <mailto:Guillaume.CharpiatREMOVETHESE23CHARACTERS@inria.fr> o équipe A&O <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/science-des-…>: Sylvain Chevallier <mailto:sylvain.chevallier@universite-paris-saclay.fr> o équipe BioInfo <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/science-des-…>: Flora Jay <mailto:Flora.JayREMOVETHESE23CHARACTERS@lisn.fr> et Fanny Pouyet <mailto:fanny%20.%20pouyet%20%5Bat%5D%20universite-paris-saclay%20.fr> o équipe LaHDAK <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/science-des-…>: Fatiha Saïs <mailto:sais@lisn.fr> * département Sciences et Technologies des Langues (STL) <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/sciences-et-…>: Aurélie Névéol <mailto:aurelie.neveol@lisn.fr> o équipe LIPS <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/sciences-et-…>: Ioana Vasilescu <mailto:ioana.vasilescu@lisn.fr> o équipe M3 <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/sciences-et-…>: Albert Rilliard <mailto:albert.rilliard@lisn.fr> o équipe SEME <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/sciences-et-…>: Agata Savary <mailto:agata.savary@universite-paris-saclay.fr> Pour plus de détails, vous pouvez consulter les fiches de poste complètes ou contacter : Les fiches de poste: - MCF : https://www.lisn.upsaclay.fr/app/uploads/1/2026/01/Fiche_emploi_CNU_27_1664… <https://www.lisn.upsaclay.fr/app/uploads/1/2026/01/Fiche_emploi_CNU_27_1664…>- PR :https://www.lisn.upsaclay.fr/app/uploads/1/2026/02/Fiche_emploi_CNU_27_20-1.pdf Pour le profil recherche : Guillaume Charpiat (guillaume.charpiat(a)lisn.fr) et Aurélie Névéol (aurelie.neveol(a)lisn.fr) Pour le profil enseignement : Fatiha Zaidi (fatiha.zaidi(a)universite-paris-saclay.fr) Les dates et lien odyssée sont disponibles ici : https://www.lisn.upsaclay.fr/recherche/concours-chercheurs-enseignants-cher… Aurélie Névéol et Guillaume Charpiat %%%%%%%%%%%%%%%%%%%%%%%%%%%% The LISN lab and Paris-Saclay University are recruiting: - an assistant professor (MCF) in Computer Science - a full professor (PR) in Computer Science The successful candidates will be based at the Faculty of Science in Orsay (Computer Science Department). LISN is an interdisciplinary research laboratory in computer science and digital sciences with internationally recognized expertise in artificial intelligence. The following topics will be given priority (in alphabetical order): Statistical learning Bioinformatics Hybrid AI, neuro-symbolic AI, knowledge representation, and reasoning Responsible digital technology Natural language processing Candidates are invited to read the LISN's multi-year AI recruitment policy: https://www.lisn.upsaclay.fr/recherche/lia-au-lisn/ and to contact the heads of the research teams and/or departments in which their research project could be carried out before March 31, 2026. * Data Science Department (SD) <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/science-des-…>: Guillaume Charpiat <mailto:Guillaume.CharpiatREMOVETHESE23CHARACTERS@inria.fr> o A&O team <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/science-des-…>: Sylvain Chevallier <mailto:sylvain.chevallier@universite-paris-saclay.fr> o BioInfo <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/science-des-…>team <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/science-des-…> : Flora Jay <mailto:Flora.JayREMOVETHESE23CHARACTERS@lisn.fr> et Fanny Pouyet <mailto:fanny%20.%20pouyet%20%5Bat%5D%20universite-paris-saclay%20.fr> o LaHDA K <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/science-des-…>team <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/science-des-…>: Fatiha Saïs <mailto:sais@lisn.fr> * Language Sciences and Technologies Department (STL) <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/sciences-et-…>: Aurélie Névéol <mailto:aurelie.neveol@lisn.fr> o LIPS <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/sciences-et-…>team <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/sciences-et-…>: Ioana Vasilescu <mailto:ioana.vasilescu@lisn.fr> o M3 <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/sciences-et-…>team <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/sciences-et-…>: Albert Rilliard <mailto:albert.rilliard@lisn.fr> o SEME team <https://www.lisn.upsaclay.fr/recherche/departements-et-equipes/sciences-et-…>: Agata Savary <mailto:agata.savary@universite-paris-saclay.fr> More details about the positions: - MCF: https://www.lisn.upsaclay.fr/app/uploads/1/2026/01/Fiche_emploi_CNU_27_1664… - PR: https://www.lisn.upsaclay.fr/app/uploads/1/2026/02/Fiche_emploi_CNU_27_20-1… Contacts for the research profile: Guillaume Charpiat (guillaume.charpiat(a)lisn.fr) and Aurélie Névéol (aurelie.neveol(a)lisn.fr) Contact for the teaching profile: Fatiha Zaidi (fatiha.zaidi(a)universite-paris-saclay.fr) The dates and link to the odyssée application platform are available here: https://www.lisn.upsaclay.fr/recherche/concours-chercheurs-enseignants-cher… Aurélie Névéol and Guillaume Charpiat

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Third Call for Papers: The 1st Workshop on Computational Affective Science (CAS 2026), co-located with the Language Resources and Evaluation Conference (LREC) 2026
by Roman Klinger 13 Feb '26

13 Feb '26

FINAL CALL FOR PAPERS: The 1st Workshop on Computational Affective Science (Deadline Extended) -------------------------------------------------------------------------------------------------- Third and Final Call for Papers: The 1st Workshop on Computational Affective Science (CAS 2026), co-located with the Language Resources and Evaluation Conference (LREC) 2026 in Palma de Mallorca, Spain, May 11-16. (Submission deadline extended to 20 Feb 2026). Website: https://casworkshop.github.io/ Contact: <cas-workshop(a)googlegroups.com> We invite submissions to the first Workshop on Computational Affective Science (CAS 2026), co-located with LREC 2026, on research related to the understanding of affect and emotions through language and computation. CAS will accept archival long and short paper submissions, featuring substantial, original, and unpublished research. We also encourage submissions of extended abstracts from researchers in the broader Affective Science community, with up to two pages of content featuring the research background/hypotheses and a description of methods/results. Extended abstracts are non-archival, offering the option for publication and presentation at other conference venues. ------------ MOTIVATION ------------ Affect refers to the fundamental neural processes that generate and regulate emotions, moods, and feeling states. Affect and emotions are central to how we organize meaning, to our behavior, to our health and well-being, and to our very survival. Despite this, and even though most of us are intimately familiar with emotions in everyday life, there is much we do not know about how emotions work and how they impact our lives. Affective Science is a broad interdisciplinary field that explores these and related questions about affect and emotions. Since language is a powerful mechanism of emotion expression, there is a growing use of language data and advanced natural language processing (NLP) algorithms to shed light on fundamental questions about emotions. The Workshop on Computational Affective Science (CAS) aims to be a dedicated venue for work focused specifically on the link between NLP and affective science. Interdisciplinary Scope: The workshop takes an interdisciplinary approach to affective science and aims at bringing together NLP researchers, scientists, and theorists from many research areas, including psychology, sociology, neuroscience, and philosophy. Although work in sentiment analysis is decades old, this work often proceeds separately and in different fields from research and theory in affective science. Meanwhile, affective scientists in psychology, sociology, neuroscience and philosophy increasingly seek to use linguistic tools to shed light on the nature of emotions, moods, and feeling states. CAS is therefore co-organized by an interdisciplinary group of researchers (spanning NLP and Affective Science) to foment collaboration at this exciting frontier of research. ------------ SUBMISSIONS ------------ We invite long and short archival paper submissions, as well as non-archival extended abstracts on a broad range of topics at the intersection of affective science and natural language processing, including but not limited to: 1. The Nature of Affect and Computational Modeling of Emotions Computational experiments that add to our understanding of affect and emotions, including findings relevant to: - theories and nature of emotion - the biology or neuroscience of emotions - appraisal models - dimensional models (valence / arousal / dominance) - models of constructed emotion - cognitive-affective architectures - emotion dynamics (emergence, intensification, decay, transitions) - emotion granularity - emotion regulation - affective embodiment - evolutionary and developmental affect - emotion–cognition interactions These areas are relevant not just to human affect, but may also apply to data animals and artificial agents. 2. Affective Data and Resources Work on compiling and annotating affect-related information in text, speech, facial and bodily expression, and physiological signals (ECG, EEG, GSR, multimodal biosensing), with a focus on text data (monolingual or multilingual) and multimodal data suitable for an NLP venue. Data from underserved languages is especially encouraged. 3. Emotion Recognition, Prediction, and Inference At the instance level: - emotion classification (discrete emotions, dimensional ratings) - emotion intensity estimation - emotion cause detection - context-aware affect inference (culture, situation, social setting) - structured emotion analysis At the aggregate level: - creating emotion arcs - determining broad trends in emotions over time or across locations - tracking emotional responses toward entities of interest (e.g., climate change) - document-level and cross-document emotion analysis - labeling social networks 4. Applications Including but not limited to: - Affect and health, psychopathology, and mental disorders - Affect and behavior/social science (e.g., interpersonal affect, empathy, group-level affect, affect contagion, computational emotion regulation) - Affect and education - Affect and literature/narratives/digital humanities - Affect and commerce 5. Explainability and Interpretability in Computational Affective Models Work aimed at improving the transparency and interpretability of affective systems. This includes understanding how models represent and infer emotions and identifying key cues driving predictions. 6. Ethics, Fairness, Theory Integration, Philosophical Implications - Bias and generalizability of affective systems across demographics - Privacy and ethics in affective data collection - Examining whether automatic NLP systems rely on current and valid theories of affect and emotion - The implications of machines modeling or simulating affect - Societal considerations surrounding affective artificial agents ------------ IMPORTANT DATES ------------ Submission deadline (**Extended**): 20 Feb 2026 Notification of acceptance: 16 March 2026 Camera Ready Paper due: 30 March 2026 Workshop date: 16 May 2026 ------------ SUBMISSION DETAILS ------------ We invite submissions for archival long and short papers, as well as non-archival extended abstracts. Archival long and short papers should feature novel and unpublished work relating to the topics detailed above. We also invite submissions of extended abstracts from researchers in the broader Affective Science community, with up to two pages of content featuring the research background/hypotheses and a description of methods/results. Extended abstracts are non-archival, offering the option for publication and presentation at other conference venues. Archival Track: Long Paper: Consists of up to 8 pages of content, with additional pages for references, limitations, ethical considerations, and appendices. Short Paper: Consists of up to 4 pages of content, with additional pages for references, limitations, ethical considerations, and appendices. (When preparing camera ready papers, you will be allowed one extra page to address comments by the reviewers.) Non-Archival Track: Extended Abstract: Up to 2 pages. ------------ SUBMISSION FORMAT ------------ All submissions must use the LREC 2026 template and follow the guidelines found at: https://lrec2026.info/authors-kit/ (Note: extended abstracts can be limited to being 1-2 pages in length). Mandatory Ethics Section: We ask all authors to include a section on Ethical Considerations in their submission, touching on the ethical concerns and broader societal impacts of the work. This discussion section will not count towards the page limit. ------------ SUBMISSION SITE ------------ All submissions must be made through the SoftConf portal: https://softconf.com/lrec2026/CAS ------------ ADDITIONAL DETAILS ------------ Website: https://casworkshop.github.io/ Attendance: The workshop will follow the attendance policy of the main conference (https://lrec2026.info/registration-policy/ ). ------------ ORGANIZERS ------------ - Christopher Bagdon, University of Bamberg, Germany - Krishnapriya Vishnubhotla, National Research Council Canada - Kristen A. Lindquist, The Ohio State University, USA - Lyle Ungar, University of Pennsylvania, USA - Roman Klinger, University of Bamberg, Germany - Saif M. Mohammad, National Research Council Canada ***Contact us at <cas-workshop(a)googlegroups.com> with any questions.***

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Deadline Extension – CfP for Workshop on Dialects in NLP: A Resource Perspective (DialRes-LREC26)
by Stavros Bompolas 13 Feb '26

13 Feb '26

***APOLOGIES FOR CROSS-POSTING*** ***************************************************************** Dear Colleagues, Due to multiple requests, we are pleased to announce a deadline extension for submissions to the Workshop on Dialects in NLP: A Resource Perspective (DialRes-LREC26), which will be held at LREC 2026 in Palma de Mallorca, Spain. The new submission deadline is now February 27, 2026. Key Information Workshop Title Dialects in NLP: A Resource Perspective (DialRes-LREC26) Event Workshop at LREC 2026 (Hybrid event — in person and online) Location Palma de Mallorca, Spain (and Online) Workshop Date May 16, 2026 Website https://dialres.github.io/dialres/<https://www.google.com/url?q=https%3A%2F%2Fdialres.github.io%2Fdialres%2F> Contact dialres-lrec26(a)googlegroups.com<https://www.google.com/url?q=mailto%3Adialres-lrec26%40googlegroups.com> Overview Dialectal and non-standard varieties pose persistent challenges for linguistic resource development. While in-depth study and large-scale resource creation for dominant or standard varieties have driven major advances in language technology, linguistic resources that adequately represent dialectal variation remain scarce. It therefore remains an open question whether standard-centric practices address dialectal variation or instead create new problems for dialects. DialRes-LREC26 invites submissions on the creation, analysis, and evaluation of dialectal resources, including—but not limited to—work that critically examines how standard-centric methodologies impact dialects in the development of linguistic resources and models. We especially encourage contributions addressing the consequences of such practices for speech and morphosyntactic modelling, OCR of dialectal and historical texts, orthographic normalisation and homogenisation, annotation practices and lemmatisation strategies that abstract away or suppress dialectal forms, as well as analyses of how these choices affect dialects and their communities methodologically, economically, and socially. The workshop focuses on problems, limitations, and trade-offs in developing dialectal resources from a linguistic perspective, while encouraging the creation and evaluation of resources in formats that enable reuse by the NLP community. Workshop Topics * Development and evaluation of dialectal oral and textual resources * Orthographic normalisation and homogenisation, including their impact on dialectal variation * Dialects vs. standard language varieties in annotation frameworks * Cross-lingual and cross-dialectal transfer and model adaptation * Resource scalability issues and techniques * Use and limitations of large language models (LLMs) in dialectal resource development * OCR for dialectal, non-standard, and historical texts: challenges, errors, and downstream effects * Resources for, and applications supporting, dialect revitalisation and preservation * Dialectal studies and teaching from a resource-oriented perspective * Working on dialectal resources: academic, financial, legal, and societal issues * Enabling and empowering dialect communities to develop their own resources Submission Information Instructions for Authors Submissions are electronic, using the Softconf START conference management system via the link: https://softconf.com/lrec2026/DialRes. They must be 4 to 8 pages long (excluding references and potential Ethics Statements) and follow the LREC stylesheet, available on the conference website on the Author’s kit page Author’s Kit<https://lrec2026.info/authors-kit/>. All templates are also available from this<https://lrec2026.info/calls/second-call-for-papers/> page. Invited Speaker Prof. Barbara Plank, LMU Munich (https://bplank.github.io/) Important Dates [updated] Submission Deadline February 27, 2026 [updated] Notification of Acceptance March 18, 2026 [updated] Camera-ready Papers Due March 28, 2026 Resubmissions from the LREC Main Conference It will also be possible to submit papers that were rejected from the LREC 2026 main conference to DialRes 2026. Such submissions must be revised to fit the scope and format of the workshop and must comply with the same anonymization requirements. Endorsements The workshop is endorsed by UniDive COST Action CA21167 and Archimedes Athena R.C. Organizing Committee * Antonios Anastasopoulos — George Mason University / Archimedes–Athena RC * Stella Markantonatou — ILSP / Archimedes–Athena RC * Angela Ralli — University of Patras / Archimedes–Athena RC * Marcos Zampieri — George Mason University * Stavros Bompolas — Archimedes–Athena RC * Vivian Stamou — Archimedes–Athena RC We look forward to receiving your contributions! Sincerely, Stavros Bompolas On behalf of the Organizing Committee of DialRes-LREC26

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[CFP] First International Workshop on Extraction from Triplet Text-Table-Knowledge Graph and associated Challenge @ ESWC 2026
by Raphaël Troncy 13 Feb '26

13 Feb '26

[Apologies for cross-postings] Call for Papers First International Workshop on Extraction from Triplet Text-Table-Knowledge Graph and associated Challenge https://ecladatta.github.io/triplet2026/ in conjunction with the 23rd European Semantic Web Conference (ESWC 2026) https://2026.eswc-conferences.org/, Dubrovnik, Croatia Important dates: - **Submission deadline**: 3 March, 2026 (11:59pm, AoE) - **Notifications**: 31 March, 2026 - **Camera-ready deadline**: 15 April, 2026 (11:59pm, AoE) - **Workshop**: Sunday 10 May OR Monday 11 May 2026 Motivation: Understanding information spread across text and table is essential for tasks such as question answering and fact checking. Existing benchmarks primarily deal with semantic table interpretation or reasoning over tables for question answering, leaving a gap in evaluating models that integrate tabular and textual information, perform joint information extraction across modalities, or can automatically detect inconsistencies between modalities. This workshop aims to provide a forum for exchanging ideas between the NLP community working on open information extraction and the vibrant Semantic Web community working on the core challenge of matching tabular data to Knowledge Graphs, on populating knowledge graphs using texts and on reasoning across text, tabular data and knowledge graphs. The workshop also targets researchers focusing on the intersection of learning over structured data and information retrieval, for example, in retrieval augmented generation (RAG) and question answering (QA) systems. Hence, the goal of the workshop is to connect researchers and trigger collaboration opportunities by bringing together views from the Semantic Web, NLP, database, and IR disciplines. Scope: The topics of interest include but are not limited to: - Semantic Table Interpretation - Automated Tabular Data Understanding - Using Large Language Models (LLMs) for Information Extraction - Generative Models and LLMs for Structured Data - Knowledge Graph Construction and Completion with Tabular Data and Texts - Analysis of Tabular Data on the Web (Web Tables) - Benchmarking and Evaluation Frameworks for Joint Text-Table Data Analysis - Applications (e.g. data search, fact-checking, Question-Answering, KG alignment) Submission Guidelines: We invite two types of submissions: 1. Full research papers (12-15 pages) including references and appendices 2. Challenge papers (6-8 pages) including references and appendices All submissions should be formatted in the CEUR layout format, https://www.overleaf.com/latex/templates/template-for-submissions-to-ceur-w… This workshop is double-blind and non-archival. Submissions are managed through EasyChair at https://easychair.org/conferences/?conf=triplet2026. All accepted papers will be presented as posters or as oral talks. **TRIPLET Challenge:** In recent years, the research community has shown increasing interest in the joint understanding of text and tabular data, often, for performing tasks such as question answering or fact checking where evidences can be found in texts and tables. Hence, various benchmarks have been developed for jointly querying tabular data and textual documents in domains such as finance, scientific publications, and open domain. While benchmarks for triple extraction from text for Knowledge Graph construction and semantic annotation of tabular data exist in the community, there remains a gap in benchmarks and tasks that specifically address the joint extraction of triples from text and tables by leveraging complementary clues across these different modalities. The TRIPLET 2026 challenge is proposing three sub-tasks and benchmarks for understanding the complementarity between tables, texts, and knowledge graphs, and in particular to propose a joint knowledge extraction and reconciliation process. #Sub-Task 1: Assessing the Relatedness Between Tables and Textual Passages The goal of this task is to assess the relatedness between tables and textual passages (within documents and across documents). For this purpose, we have constructed LATTE (Linking Across Table and Text for Relatedness Evaluation), a human annotated dataset comprising table–text pairs with relatedness labels. LATTE consists of 7,674 unique tables and 41,880 unique textual paragraphs originating from 3,826 distinct Wikipedia pages. Each text paragraph is drawn from the same or contextually linked pages as the corresponding table, rather than being artificially generated. LATTE provides a challenging benchmark for cross-modal reasoning by requiring classification of related and unrelated table–text pairs. Unlike prior resources centered on table-to-text generation or text retrieval, LATTE emphasizes fine-grained semantic relatedness between structured and unstructured data. The Figure below provides an example, using a web-annotation tool we developed, of how we identify the relatedness between the sentence containing the entity AirPort Extreme 802.11n (highlighted in Orange) and the data table providing information about output power and frequency for this entity. Participants are provided with tables and textual passages that would need to be ranked. The evaluation will use metrics such as P@k, R@k and F1@k. Go to https://www.codabench.org/competitions/12776/ and enroll to participate in this Task. #Sub-Task 2: Joint Relation Extraction Between Texts and Tables The goal of this task is to automatically extract knowledge jointly from tables and related texts. For this purpose, we created ReTaT, a dataset that can be used to train and evaluate systems for extracting such relations. This dataset is composed of (table, surrounding text) pairs extracted from Wikipedia pages and has been manually annotated with relation triples. ReTaT is organized in three subsets with distinct characteristics: domain (business, telecommunication and female celebrities), size (from 50 to 255 pairs), language (English vs French), type of relations (data vs object properties), close vs open list of relation, size of the surrounding text (paragraph vs full page). We then assessed its quality and suitability for the joint table-text relation extraction task using Large Language Models (LLMs). Given a Wikipedia page containing texts and tables and a list of predicates defined in Wikidata, a participant system should extract triples composed of mentions located partly in the text and partly in the table and disambiguated with entities and predicates identified in the Wikidata reference knowledge graph. For example, in the Figure below, an annotation triple <Q13567390, P2109, 24.57> is associated with mentions highlighted in orange (subject), blue (predicate) and green (object) to annotate the document available at https://en.wikipedia.org/wiki/AirPort_Extreme. Similar to the Text2KGBench evaluation (https://link.springer.com/chapter/10.1007/978-3-031-47243-5_14), and because the set of triples are not exhaustive for a given sentence, to avoid false negatives, we follow a locally closed approach by only considering the relations that are part of the ground truth. The evaluation then uses metrics such as P, R and F1. Go to https://www.codabench.org/competitions/12936/ and enroll to participate in this Task. # Sub-Task 3: Detecting Inconsistencies Between Texts, Tables and Knowledge Graphs The goal of this task is to check the consistency of knowledge extracted from tables and texts with existing triples in the Wikidata knowledge graph. Different kind of inconsistencies will be considered in this task. Participants to this task will be able to report on their findings in their system paper. See the Figure at https://ecladatta.github.io/images/triplet_annotation_tool.png # Data & Evaluation: For the first 2 sub-tasks, we have released a training dataset with ground-truth annotations, enabling participant teams to develop machine learning-based systems, and in particular for training purposes and for hyperparameter optimizations and internal validations. A separate blind test dataset will remain private and be used for ranking the submissions. Participants should register on Codabench and then enroll for each sub-task separately (Task 1: https://www.codabench.org/competitions/12776/ and Task 2: https://www.codabench.org/competitions/12936/). Each team are allowed a limited number of daily submissions, and the highest achieved accuracy will be reported as the team's final result. We encourage participants to develop open-source solutions, to utilise and fine-tune pre-trained language models and to experiment with LLMs of various size in zero-shot or few-shot settings. # Challenge Important Dates: - Release of training set: 13 February 2026 - Deadline for registering to the challenge: 15 March 2026 - Release of test set: 24 March 2026 - Submission of results: 10 April 2026 - System Results & Notification of Acceptance: 17 April 2026 - Submission of System Papers: 28 April 2026 - Presentations @ TRIPLET Workshop: May 2026 Workshop Organizers - Raphael Troncy (EURECOM, France) - Yoan Chabot (Orange, France) - Véronique Moriceau (IRIT, France) - Nathalie Aussenac-Gilles (IRIT, France) - Mouna Kamel(IRIT, France) Contact: For discussions, please use our Google Group, https://groups.google.com/g/triplet-challenge The workshop is supported by the ECLADATTA project funded by the French National Funding Agency ANR under the grant ANR-22-CE23-0020. -- Raphaël Troncy EURECOM, Campus SophiaTech Data Science Department 450 route des Chappes, 06410 Biot, France. e-mail: raphael.troncy(a)eurecom.fr & raphael.troncy(a)gmail.com Tel: +33 (0)4 - 9300 8242 Fax: +33 (0)4 - 9000 8200 Web: http://www.eurecom.fr/~troncy/

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PostDoc/Senior Researcher for Explainable NLP and XAI (m/f/d/x)
by Simon Ostermann 13 Feb '26

13 Feb '26

Dear colleagues, We are looking for a postdoctoral researcher with experience in explainable AI and human-computer interaction who will pursue an independent research agenda, acquire third-party funding, and eventually establish their own research group. More details can be found in our application portal https://jobs.dfki.de/en/vacancy/en-senior-researcher*in-fur-erklarbare-spra… *The application deadline is March 1st.* The position is initially assigned to the Efficient and Explainable NLP group in the Multilinguality and Language Technology research department at DFKI in Saarbrücken, which is headed by Dr. Simon Ostermann (https://www.dfki.de/en/web/research/research-departments/multilinguality-an…). The group works on national and international research and development projects in the field of explainable and efficient language processing. The environment offers a very active publication culture, high methodological expertise, a focus on basic research, and close ties to current developments in NLP and AI research. The focus of the position is on the research and development of explainable AI systems with a special emphasis on user perspectives and interaction. Relevant topics include explainable multimodal models, user-centred explanation approaches, and the generation of rationales for complex model decisions. The specific design of the research agenda leaves room for your own ideas and new research directions. A central goal of the position is to establish or expand an independent scientific profile. This includes, in particular, the development of your own project ideas, the acquisition of third-party funding, and the preparation and establishment of your own research group in the aforementioned field within the MLT research department. The position is ideally to be filled on 1 April 2026; later employment is possible. The position is initially limited to three years, but may be made permanent if the candidate successfully assists in acquiring third-party funding. Your tasks - You will work on independent research questions in the field of explainable AI and interaction as part of a BMFTR-funded project. - You will develop and evaluate user-centred explanatory approaches for complex AI models. - You will publish your research at leading international conferences. - You will design and apply for independent third-party funded projects in collaboration with other members of the group. - You will actively participate in developing your own research agenda and take on scientific leadership tasks in the future. - You will supervise master's theses and doctoral dissertations and participate in teaching. Your qualifications - Completed doctorate in computer science, AI, computational linguistics, human-computer interaction or a related field. - Very good knowledge of explainable AI, interpretability and language processing. - High motivation for independent research and profile building. - Initial experience in acquiring third-party funding. - A convincing track record of publications in the field of NLP and explainable AI, ideally at leading international conferences such as ACL, EMNLP, NAACL, EACL, COLING, CHI, FAccT, NeurIPS, ICLR or ICML. - Very good publication and communication skills. Your benefits - An excellent research environment with high international visibility. - Great scientific freedom while being part of an established research group. - Active support in acquiring third-party funding and setting up your own working group. - An interdisciplinary network at the interface of AI research, language processing and human-computer interaction. - A young, motivated and collegial team. - A working environment in which we place great value on positive, respectful and constructive collaboration. The German Research Center for Artificial Intelligence (DFKI) has operated as a non-profit, Public-Private-Partnership (PPP) since 1988. DFKI combines scientific excellence and commercially-oriented value creation with social awareness and is recognized as a major "Center of Excellence" by the international scientific community. In the field of artificial intelligence, DFKI has focused on the goal of human-centric AI for more than 35 years. Research is committed to essential, future-oriented areas of application and socially relevant topics. DFKI encourages applications from people with disability; DFKI intends to increase the proportion of female employees in the field of science and encourages women to apply for this position.

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3rd CfP and deadline extension: CMLC-12: 12th Workshop on the Challenges in the Management of Large Corpora
by Marc Kupietz 13 Feb '26

13 Feb '26

12th Workshop on the Challenges in the Management of Large Corpora The next meeting of CMLC (see also http://corpora.ids-mannheim.de/cmlc.html ) will be held as part of the LREC-2026 conference [3] in Palma, Mallorca. 3rd Call for Papers and deadline extensionImportant dates * Deadline for paper submission: the 16th 25th of February 2026 (Monday, 23:59 UTC) * Notification of acceptance: the 12th of March 2026 (Thursday) * Deadline for the submission of camera-ready papers: the 30th of March 2026 (Monday) * Meeting: the 11th of May, morning slot Paper submission * We invite anonymised extended abstracts for oral presentations on the topics listed above, as PDF created according to LREC-2026 templates [1]. Length and content: 4 to 8 pages in length, excluding acknowledgements, references, potential Ethics Statements and discussion on Limitations. Appendices or supplementary material are not permitted during the initial submission phase, as papers should be self-contained and reviewable on their own. However, appendices and supplementary material will be allowed in the final, camera-ready version of the paper. * CMLC has always reserved a track for national corpus project reports, and to this end, we invite poster proposals of 500-750 words. National project reports need not be anonymised. * Submissions are accepted solely through the LREC START system [2]. * A volume of proceedings will be published online by ELRA. Oral and poster contributions will have equal status. Workshop description As in the previous CMLC meetings, we wish to explore common areas of interest across a range of issues in language resource management, corpus linguistics, natural language processing, natural language generation, and data science. Large textual datasets require careful design, collection, cleaning, encoding, annotation, storage, retrieval, and curation to be of use for a wide range of research questions and to users across a number of disciplines. A growing number of national and other very large corpora are being made available, many historical archives are being digitised, numerous publishing houses are opening their textual assets for text mining, and many billions of words can be quickly sourced from the web and online social media. A mixed blessing of the times is that much of those texts, in mono- and multi-lingual arrangements can now be created automatically by exploiting Large Language Models at various scales. That, on the one hand, makes it possible to inflate the amounts of data where normally data would be scarce: in under-resourced languages or language varieties, in specific genres or for intricate and rarely attested constructions. On the other hand, such procedures immediately raise concerns regarding the authenticity and quality of such data, casting doubt on the possibility of adequately (truthfully, verifiably, reproducibly) addressing the kind of research questions that provoked the rapid but tainted increase of the available data volumes in the first place. Similar doubts may be directed at mass creation of secondary and tertiary data ordinarily crucial for linguistic research: apart from potential legal constraints on the use of the initial amounts of human-created data, new questions arise as to the legal status of the derived data, the ways to create e.g. provenance metadata of the derived resources, and the level of trust regarding mass-produced grammatical (and other) annotation layers. These new as well as more traditional questions lie at the base of the list of topics that management of large corpora (for any currently suitable definition of “large”) invokes or at least strongly brushes against. Topics of interest This year's event adds new items to the standard range of CMLC themes and addresses some of LREC-2026 focus topics: * Interoperability and accessibility How to make corpora as accessible as possibleInteroperable APIs for query and analysis softwareProvision of multiple levels of access for different tasks * Machine/Deep Learning Data preparation for machine learning inputCreation, curation, maintenance and dissemination of language models based on machine learning (e.g. word embeddings and entire deep learning networks)Legal issues concerning language model distribution * Linguistic content challenges Dealing with the variety of language: multilinguality, minority and/or underrepresented languages, historical texts, noisy OCR texts, user-generated content, etc.Diversity and inclusion in language resourcesIntegration of human computation (crowdsourcing) and automatic annotationQuality management of annotationsEnsuring linguistic integrity of data through deduplication, correction of typos and errors, removal of incomplete or malformed sentences, and filtering harmful, offensive and toxic content, etc.Integrating different linguistic data types (text, audio, video, facsimiles, experimental data, neuroimaging data, …) * Technical challenges Storage and retrieval solutions for large text corpora: primary data (potentially including facsimiles, etc.), metadata, and annotation dataCorpus versioning and release managementScalable and efficient NLP tooling for annotating and analysing large datasets: distributed and GPGPU computing; using big data analysis frameworks for language processingDealing with streaming data (e.g. Social Media) and rapidly changing corporaEnvironmental impact of big language data computingEngineering and management of research software * Exploitation challenges Legal and privacy issuesQuery languages, data models, and standardisationLicensing models of open and closed data, coping with intellectual property restrictionsInnovative approaches for aggregation and visualisation of text analyticsRepurposing or extending application areas of existing corpora and tools National corpus initiatives In the tradition of CMLC, we invite reports on national corpus initiatives; submitters of these reports should be prepared to present a poster. Given that it's been a while since the last round, we would be happy to have a little "What's the news?" session, and we cordially invite both our veteran presenters as well as colleagues who have not yet introduced their national corpus projects, Our poster sessions are usually scheduled to overlap with the coffee break, to ensure informal atmosphere and to maximally use the time slot available to us. A flash presentation section is plan for just before the poster session: ca. 3 minutes for the highlights. LRE 2026 Map and the "Share your LRs!" initiative 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). Programme Committee * Laurence Anthony (Waseda University, Japan) * Vladimír Benko (Slovak Academy of Sciences) * Felix Bildhauer (IDS Mannheim) * Mark Davies (English-Corpora.org) * Nils Diewald (IDS Mannheim) * Kaja Dobrovoljc (University of Ljubljana / Jožef Stefan Institute) * Jarle Ebeling (University of Oslo) * Tomaž Erjavec (Jožef Stefan Institute, Ljubljana) * Andrew Hardie (Lancaster University, UK) * Serge Heiden (ENS de Lyon) * Ulrich Heid (University of Hildesheim) * Nancy Ide (Vassar College / Brandeis University) * Olha Kanishcheva (Heidelberg University) * Gražina Korvel (Vilnius University) * Natalia Kocyba (Samsung Poland) * Michal Křen (Charles University, Prague) * Anna Latusek (ICS PAS, Warsaw) * Paul Rayson (Lancaster University) * Laurent Romary (INRIA) * Thomas Schmidt (University of Duisburg-Essen) * Serge Sharoff (University of Leeds) * Maria Shvedova (Kharkiv Polytechnic Institute / University of Jena) * Irena Spasić (Cardiff University) * Martin Wynne (University of Oxford) Organising Committee * 📩 Piotr Bański (IDS Mannheim) * 📩 Dawn Knight (Cardiff University) * 📩 Marc Kupietz (IDS Mannheim) * 📩 Andreas Witt (IDS Mannheim) * 📩 Alina Wróblewska (ICS PAS, Warsaw) [1] LREC-2026 templates https://lrec2026.info/authors-kit/ [2] LREC START system https://softconf.com/lrec2026/CMLC2026/ [3] LREC-2026 conference https://lrec2026.info/

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Final CfP: Gaze4NLP - The Second International Workshop on Gaze Data and Natural Language Processing
by cagri coltekin 12 Feb '26

12 Feb '26

**Final Call for Papers (with extended deadline)** Gaze4NLP - The Second Workshop on Gaze Data and Natural Language Processing 12 May 2026, Palma de Mallorca, Spain (co-located with LREC 2026) https://gaze4nlp.github.io/Gaze4NLP2026/ The Second Workshop on Gaze Data and Natural Language Processing (Gaze4NLP) invites papers of a theoretical or experimental nature describing research methodologies by employing interdisciplinary perspectives, including computer science and engineering perspectives and cognitive sciences, and identifying challenges to resolve in the intersection of the two domains: eye tracking and NLP. Gaze4NLP aims to bring together researchers conducting research on eyes on eyes on text and NLP; and establishing bridges between them for identifying future venues of research. Workshop webpage: https://gaze4nlp.github.io/Gaze4NLP2026/ Important Dates Workshop paper submission deadline: 23 February 2026 Workshop paper acceptance notification: 16 March 2026 Workshop paper camera-ready versions: 30 March 2026 Workshop date: 12 May 2026 All deadlines are 11:59PM UTC-12:00 (anywhere on Earth) Topics for the workshop will include, but are not limited to: - Investigating the pillars for bridging the gap between the research on eyes on text and NLP. Study how to expand research methodologies by employing interdisciplinary perspectives, including computer science and engineering perspectives and cognitive sciences, and identify challenges, issues to resolve. - Exploring new areas so that both fields benefit from each other better than the past, identifying novel domains of exploration for further research. - Discussing how to develop cognitively inspired models that align human reading data with LLMs. Submissions We solicit regular workshop papers, which will be included in the proceedings as archival publications. The length of the papers should be between 4 and 8 pages (excluding references). The submissions should not include any appendices. Accepted papers will be presented in the form of either oral or poster presentations. Please note that camera-ready papers are allowed an additional page of content to address reviewer comments, and unlimited pages for appendices. The workshop proceedings will be part of the ACL anthology. Accepted papers will also be given an opportunity with an extended version to be published as part of an edited book. Submissions will be handled via the START Conference Manager. - Submission link: https://softconf.com/lrec2026/Gaze4NLP/ All submissions should follow the LREC style guidelines. We strongly recommend the use of the LaTeX style files, OpenDocument, or Microsoft Word templates created for LREC: <https://lrec2026.info/authors-kit/>. All papers must be anonymous, i.e., not reveal author(s) on the title page or through self-references. So, e.g., “We previously showed (Smith, 2020)”, should be avoided. Instead, use citations such as “Smith (2020) previously showed”. LRE-Map and Sharing Language Resources 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). Organization Committee: Cengiz Acarturk, Jagiellonian University, Poland Jamal Nasir, University of Galway, Ireland Burcu Can, University of Stirling, Scotland, UK Cagri Coltekin, University of Tubingen, Germany

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[LREC2026 Tutorial] Low-Resource, High-Impact: Building Corpora for Inclusive Language Technologies
by Daryna Dementieva 12 Feb '26

12 Feb '26

Dear colleagues, Do you care about improving language technologies beyond mainstream languages? Do you wonder how to collect data for low-resource languages? Or how to create the first translation system? And then adapt efficiently to various downstream tasks? We are pleased to announce an upcoming LREC2026 tutorial “Low-Resource, High-Impact: Building Corpora for Inclusive Language Technologies.” This tutorial is aimed at NLP practitioners, researchers, and developers working with multilingual and low-resource languages who are interested in building more equitable, inclusive, and socially impactful language technologies. **Tutorial overview** The tutorial covers the full lifecycle of NLP technologies development for a language, including: * Data collection and corpus creation (e.g., web crawling and annotation) * Parallel sentence mining and machine translation * Downstream applications such as text classification and multimodal reasoning * Strategies for addressing data scarcity, cultural variance, and reproducibility * Fair and community-informed development practices **Who should attend** * Researchers and practitioners in NLP and multilingual technologies * Corpus builders and linguists working on underrepresented languages * Developers interested in low-resource or inclusive NLP * Students and early-career researchers **Scope and highlights** * Case studies spanning 10+ languages from diverse language families and geopolitical contexts * Coverage of both digitally resource-rich and severely underrepresented languages * Emphasis on hands-on methods and applied modeling frameworks **Save the date and place**: Saturday, 16 May 2026, morning session, Room 6 More information: https://tum-nlp.github.io/low-resource-tutorial/ Stay tuned for our website – we will fully open-source the tutorial materials! Additionally, we would like to have an overview of overall practices and challenges researchers facing working with non-mainstream languages. If you are such a researcher, you are working on a very surprising language, or just have experience to share about the topic, please, fill in this form to participate in the interview: https://forms.gle/L81hpvZGfemyMjtX7 **Organisers**: Ekaterina (Katya) Artemova, Toloka.ai Laurie Burchell, Common Crawl Foundation Daryna Dementieva, Technical University of Munich Shu Okabe, Technical University of Munich Mariya Shmatova, Toloka.ai Pedro Ortiz Suarez, Common Crawl Foundation See you at LREC! Best regards, Daryna Dementieva On behalf of Tutorial Organisers

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Edge Hill Corpus Research Group: Meeting #20
by Costas Gabrielatos 12 Feb '26

12 Feb '26

The next meeting of the Edge Hill Corpus Research Group will take place online (via MS Teams) on Friday 6 March 2026, 10:00-11:30 am (GMT<https://time.is/United_Kingdom>). Topic: LLMs, Corpus Linguistics, and Language Learning Speaker: Peter Crosthwaite<https://languages-cultures.uq.edu.au/profile/2845/peter-crosthwaite> (University of Queensland, Australia) Title: Corpora, Prompts, and Pedagogy: Human-AI Text Comparison in Applied Linguistics The abstract and registration link are here: https://sites.edgehill.ac.uk/crg/next Attendance is free. Registration closes on Wednesday 4 March. If you have problems registering, or have any questions, please email the organiser, Costas Gabrielatos (gabrielc(a)edgehill.ac.uk<mailto:gabrielc@edgehill.ac.uk>). ________________________________ Edge Hill University<http://ehu.ac.uk/home/emailfooter> Modern University of the Year, The Times and Sunday Times Good University Guide 2022<http://ehu.ac.uk/tef/emailfooter> University of the Year, Educate North 2021/21 ________________________________ This message is private and confidential. If you have received this message in error, please notify the sender and remove it from your system. Any views or opinions presented are solely those of the author and do not necessarily represent those of Edge Hill or associated companies. Edge Hill University may monitor email traffic data and also the content of email for the purposes of security and business communications during staff absence.<http://ehu.ac.uk/itspolicies/emailfooter>

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Postdoc position: AI in vocational training/education
by Antje Schweitzer 12 Feb '26

12 Feb '26

Dear all, I would like to draw your attention to the position announced below. We are searching for a postdoc or an advanced PhD student whose research interests align with the goals of the project. The position is funded until the end of 2027. Since the position requires very good German language proficiency, the details below are posted in German. Best regards, Antje Schweitzer -- Dr. Antje Schweitzer IMS Uni Stuttgart 0711-685 81376 https://www.ims.uni-stuttgart.de/~schweitz Stellenausschreibung PostDoc-Stelle (m/w/d, E 13 TV-L, 100%) im Projekt MEKI Bereich KI in der Berufsbildung 1. Mai 2026 bis 31. Dezember 2027 Universität Stuttgart Institut für Maschinelle Sprachverarbeitung Arbeitsgruppe Digitale Phonetik, Prof. Dr. Thang Vu, https://www.ims.uni-stuttgart.de/en/institute/team/Vu-00002/ In Deutschland werden laut aktuellen Prognosen bis zum Jahr 2035 ganze 7 Millionen Arbeitskräfte fehlen. Dieser dramatische Fachkräftemangel wird davon verstärkt, dass immer mehr junge Erwachsene keinen Berufsabschluss haben. Außerdem brechen bis zu 28% der Auszubildenden ihre Ausbildung vorzeitig ab. Dies wollen wir ändern! Im Projekt MEKI (Mehr erreichen mit KI) entwickeln wir eine KI-unterstützte (open source) Lernsoftware, die vor allem schwächeren Auszubildenden in der Berufsschule helfen soll, ihre Ausbildung erfolgreich zu beenden. Das Projekt konzentriert sich auf den gewerblich-technischen Bereich, entwickelt aber Konzepte, die auf andere Bereiche übertragen werden können. Es wird vom BMBSFJ im Rahmen der InnoVET PLUS-Förderrichtlinie gefördert. Wir arbeiten in MEKI mit vier Verbundpartnern (zwei Industrie- und Handelskammern (IHKs) sowie der TU München und der LMU München) zusammen. Die IHKs sowie die LMU sind vor allem für die Ermittlung der Bedarfe zuständig; die Software wird von der TU und uns gemeinsam entwickelt, wobei unsere Hauptzuständigkeit die KI-Features sind. Im Projekt ist zum nächstmöglichen Zeitpunkt, möglichst ab Mai 2026, bis zum Ende der Projektlaufzeit im Dezember 2027, eine PostDoc-Stelle (m/w/d, E 13 TV-L, 100%) zu besetzen. Nach Absprache ist auch eine Beschäftigung in Teilzeit (bei gleicher Laufzeit) denkbar. Wir suchen eine*n motivierte*n Kolleg*in, die*der Lust hat, sich außerhalb der akademischen Bildung zu engagieren und ein gesellschaftliches Problem mithilfe von KI-Methoden anzugehen. Der Einsatz von KI in der Bildung eröffnet innovative Möglichkeiten wie individualisiertes und multimodales Lernen oder Gamification zur Motivationssteigerung. Gewünschtes Profil: • sehr gute Deutschkenntnisse in Wort und Schrift • Lust auf Teamarbeit • sehr gute Kommunikationsfähigkeit • Gewissenhaftigkeit und analytisches Denken • eine hervorragende Promotion (Summa oder Magna cum laude) im Bereich Maschinelle Sprachverarbeitung, Computational Linguistics, Digital Humanities, Data Science, Softwareentwicklung oder in einem vergleichbaren Bereich • umfassende Erfahrung mit dem Einsatz von LLMs im Rahmen der Promotion • sehr gute Programmierkenntnisse, insbesondere in Python • Erfahrung im Bereich multimodale Generierung • Erfahrung mit Deep Learning ist ein Plus, aber nicht unbedingt notwendig • Erfahrung mit Git • Erfahrung in Frontend-Entwicklung, z.B. React/Typescript Aufgaben im Projekt: • Konzeption und Implementierung neuer KI-basierter Software-Features insbesondere im Bereich multimodales Lernen • Beteiligung an der projektbegleitenden Evaluierung von KI-Features der Software • Gemeinsames Testen der Software im Rahmen von Nutzungsstudien mit Projektpartnern • Dokumentation und Veröffentlichung von Projektergebnissen • Aktive Zusammenarbeit mit den Verbundpartnern sowie aktive Teilnahme an verbundinternen Treffen • Entwicklung neuer Ideen zum Einsatz von KI in der Bildung für zukünftige Projekte Das bieten wir: • Ein diverses, engagiertes Team und angenehme Arbeitsatmosphäre mit exzellenter Forschungsumgebung in internationalem und interdisziplinärem Umfeld am Institut für Maschinelle Sprachverarbeitung der Universität Stuttgart • Moderne Themen mit gesellschaftlicher Relevanz im Bereich Bildung • Kooperationsmöglichkeiten und Kontakt zu anderen interdisziplinären Projekten im Bereich Deep Learning und LLMs am Institut für Maschinelle Sprachverarbeitung • Unterstützung durch studentische Hilfskräfte • Weiterbildungsmöglichkeiten im Bereich Lehre Bewerbungsverfahren Wir bitten um eine PDF-Datei mit: • einem kurzen Motivationsschreiben, in dem die Forschungsinteressen dargelegt sind, • dem Lebenslauf mit Publikationsliste, • den Kontaktdaten von ein bis zwei Referenzpersonen. Die Universität Stuttgart steht für gelebte Vielfalt und Chancengerechtigkeit sowie für die Vereinbarkeit von Beruf und Familie. Bewerberinnen werden bei gleicher Eignung, Befähigung und fachlicher Leistung in Bereichen, in denen Frauen unterrepräsentiert sind, bevorzugt berücksichtigt. Schwerbehinderte Bewerber*innen werden bei gleicher Qualifikation vorrangig eingestellt. Bewerbungen von Menschen anderer Nationalitäten oder mit Migrationsgeschichte begrüßen wir ausdrücklich. Bewerbungen sind zu richten an: Antje Schweitzer, <mailto:antje.schweitzer@ims.uni-stuttgart.de> Bewerbungen, die bis zum 28. Februar 2026 eingehen, werden vollständig berücksichtigt. Die Stelle bleibt bis zur Besetzung offen. Informationen zur Umgebung Die Universität Stuttgart ist eine technisch orientierte Universität. Sie ist besonders für Ingenieurwissenschaften und verwandte Themen bekannt, wobei ihre Informatikabteilung sowohl national als auch international einen hohen Stellenwert einnimmt. Das Institut für Maschinelle Sprachverarbeitung (IMS), das zur Fakultät für Informatik und Elektrotechnik gehört, ist eines der größten akademischen Forschungsinstitute für natürliche Sprachverarbeitung in Deutschland. Es schlägt die Brücke zwischen der Grundlagenforschung zu Sprache und der Entwicklung von Technologien für die Gesellschaft. Mit 6 Professuren, über 50 Wissenschaftler*innen und 200 Studierenden in den Studiengängen B.Sc. und M.Sc. Computational Linguistics gehört das IMS zu den größten Standorten der Computerlinguistik in Deutschland und Europa. Stuttgart ist bekannt für seine starke Wirtschaft und abwechslungsreiche Kulturszene bei überschaubarer Größe. Sehenswert ist auch die Lage inmitten einer Vielzahl von Hügeln und Weinbergen. Stuttgart ist eine lebhafte Stadt mit einer aktiven Bar- und Clubszene und einem gut ausgebauten öffentlichen Nahverkehr. Mit dem Zug ist Stuttgart gut an viele andere interessante Orte angebunden, zum Beispiel München und Köln (~2 Stunden), Paris (~3,5 Stunden), Berlin (~5,5 Stunden), Straßburg (1 Stunde) oder den Bodensee (2 Stunden). Förderprogramm des BMBFSFJ: https://www.inno-vet.de/innovet/de/innovet_plus/innovet-plus_node.html Projektbeschreibung beim Verbundkoordinator IHK Reutlingen: https://www.reutlingen.ihk.de/ausbildung/azubis-hier-lang/digitale-lernange… Institut für Maschinelle Sprachverarbeitung: https://www.ims.uni-stuttgart.de Digitale Phonetik https://www.ims.uni-stuttgart.de/institut/arbeitsgruppen/dp/

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