LM4DH 2025 : The First Workshop on Natural Language Processing and Language Models for Digital Humanities (LM4DH 2025) @ RANLP_2025

Call for papers: The First Workshop on Natural Language Processing and Language Models for Digital Humanities

(LM4DH_2025) @ RANLP_2025

Date: 11th- to 13th September 2025 (TBC)

Venue : Varna, Bulgaria

Website: https://www.clarin.eu/event/2025/clarin-workshop-ranlp-2025
Submissions Portal: https://softconf.com/ranlp25/LM4DH2025/



Digital Humanities has emerged as an interdisciplinary field of research, serving as an intersection of computer science with many other fields such as linguistics, social sciences, history, psychology, etc. With the development of Large Language Models (LLMs), state-of-the-art Natural Language Processing (NLP) tasks such as entity recognition, sentiment analysis, and text summarisation have been significantly enhanced, offering powerful tools to analyse and interpret complex historical and cultural data. These developments offer transformative capabilities for analysing and interpreting complex historical and cultural datasets, including oral histories, archival documents, and literary texts. These advancements provide powerful tools for analysing and interpreting intricate historical, cultural, and social data, enabling researchers to identify patterns, extract meaningful relationships, and generate interpretations at unprecedented scale and precision.



This workshop aims to provide a common platform for researchers, practitioners, and students from diverse disciplines to collaboratively explore and apply AI-driven techniques in the Digital Humanities. Through interdisciplinary discussion, the event aims to generate creative approaches, exchange best practices, and create a community committed to furthering AI-based research on human culture and history. The focus of the workshop is on applying natural language processing techniques to digital humanities research. The topics can be anything of digital humanities interest with a natural language processing or LLM-based application. We expect contributions related (but not limited) to the following topics:



Text analysis and processing related to the humanities using computational methods
Usage of the interpretability of large language models' output for DH-related tasks
Dataset creation and curation for NLP (e.g. digitisation, datafication, and data preservation
Automatic error detection, correction, and normalisation of textual data
Generation and analysis of literary works such as poetry and novels
Analysis and detection of text genres
Emotion analysis for the humanities and literature
Modelling of information and knowledge in the Humanities, Social Sciences, and Cultural Heritage
Low-resource and historical language processing
Search for scientific and/or scholarly literature
Profiling and authorship attribution

Submission & Publication

All papers must represent original and unpublished work that is not currently under review. Papers will be evaluated according to their significance, originality, technical content, style, clarity, and relevance to the workshop.

Submissions must follow the RANLP 2025 submission guidelines, using ACL-style templates (LaTeX or MS Word).

Paper must be submitted using SoftConf at https://softconf.com/ranlp25/LM4DH2025/

All papers will be double-blind peer reviewed. Authors of the accepted papers will present their work in either the oral or poster session. All accepted papers will appear in the workshop proceedings that will be published in ACL Anthology.

Important Dates

Paper submission deadline: 20th July 2025
Notification of acceptance: 2nd August 2025
Camera-ready paper: 20th August 2025
Workshop date: 11th September 2025

Organising Committee

Isuri Anuradha, Lancaster University, UK
Francesca Frontini, CNR-ILC, Italy & CLARIN ERIC
Paul Rayson, Lancaster University, UK
Ruslan Mitkov, Lancaster University, UK
Deshan Sumanathilake, Swansea University, UK





This workshop has been organised with the generous support and coordination of CLARIN-EU.

Gmail: [email protected]