Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert, Barna Pásztor, Bettina Messmer, Dhia Garbaya, Eduard Frank Ďurech, Ido Hakimi, Juan Garcia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabolčec, Yixuan Xu, Michael Aerni, Badr AlKhamissi, Inés Altemir Marinas, Mohammad Hossein Amani, Matin Ansaripour, Ilia Badanin, Harold Benoit, Emanuela Boros, Nicholas John Browning, Fabian Bösch, Maximilian Böther, Niklas Canova, Camille Challier, Clément Charmillot, Jonathan Coles, Jan Milan Deriu, Arnout Devos, Lukas Drescher, Daniil Dzenhaliou, Maud Ehrmann, Dongyang Fan, Simin Fan, Silin Gao, Miguel Gila, María Grandury, Diba Hashemi, Alexander Miserlis Hoyle, Jiaming Jiang, Mark Klein, Andrei Kucharavy, Anastasiia Kucherenko, Frederike Lübeck, Roman Machacek, Theofilos Ioannis Manitaras, Andreas Marfurt, Kyle Matoba, Simon Matrenok, Henrique Mendonça, Fawzi Roberto Mohamed, Syrielle Montariol, Luca Mouchel, Sven Najem-Meyer, Jingwei Ni, Gennaro Oliva, Matteo Pagliardini, Elia Palme, Andrei Panferov, Léo Paoletti, Marco Passerini, Ivan Pavlov, Auguste Poiroux, Kaustubh Ponkshe, Nathan Ranchin, Javier Rando, Mathieu Sauser, Jakhongir Saydaliev, Mukhammadali Sayfiddinov, Marian Schneider, Stefano Schuppli, Marco Scialanga, Andrei Semenov, Kumar Shridhar, Raghav Singhal, Anna Sotnikova, Alexander Sternfeld, Ayush Kumar Tarun, Paul Teiletche, Jannis Vamvas, Xiaozhe Yao, Hao Zhao, Alexander Ilic, Ana Klimovic, Andreas Krause, Caglar Gulcehre, David Rosenthal, Elliott Ash, Florian Tramèr, Joost VandeVondele, Livio Veraldi, Martin Rajman, Thomas C. Schulthess, Torsten Hoefler, Antoine Bosselut, Martin Jaggi, Imanol Schlag. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Digital knowledge platforms increasingly shape how cultural heritage and historical responsibility are represented and remembered, yet they are vulnerable to subtle forms of narrative manipulation that rarely appear as explicit misinformation. We present a new dataset of English Wikipedia revision pairs designed to support the systematic study of cultural heritage manipulation in the context of the Russo--Ukrainian conflict. The resource captures fine-grained textual changes through which cultural attribution, agency, and legitimacy are incrementally reframed within a collaborative encyclopedic infrastructure. By providing paired revisions, editorial context, and consistent annotations, the dataset enables analysis of discursive interventions that remain procedurally legitimate while producing cumulative cultural effects. The dataset is released to support research in information retrieval, natural language processing, and computational social science on detecting and understanding subtle narrative change in large-scale, collaborative knowledge systems. The resource is available at https://zenodo.org/records/19883068.

This article considers the interdisciplinary opportunities and challenges of working with digital cultural heritage, such as digitized historical newspapers, and proposes an integrated digital hermeneutics workflow to combine purely disciplinary research approaches from computer science, humanities, and library work. Common interests and motivations of the above-mentioned disciplines have resulted in interdisciplinary projects and collaborations such as the NewsEye project, which is working on novel solutions on how digital heritage data is (re)searched, accessed, used, and analyzed. We argue that collaborations of different disciplines can benefit from a good understanding of the workflows and traditions of each of the disciplines involved but must find integrated approaches to successfully exploit the full potential of digitized sources. The paper is furthermore providing an insight into digital tools, methods, and hermeneutics in action, showing that integrated interdisciplinary research needs to build something in between the disciplines while respecting and understanding each other's expertise and expectations.

Named entity processing over historical texts is more and more being used due to the massive documents and archives being stored in digital libraries. However, due to the poor annotated resources of historical nature, information extraction performances fall behind those on contemporary texts. In this paper, we introduce the development of the NewsEye resource, a multilingual dataset for named entity recognition and linking enriched with stances towards named entities. The dataset is comprised of diachronic historical newspaper material published between 1850 and 1950 in French, German, Finnish, and Swedish. Such historical resource is essential in the context of developing and evaluating named entity processing systems. It evenly allows enhancing the performances of existing approaches on historical documents which enables adequate and efficient semantic indexing of historical documents on digital cultural heritage collections.

In this paper, we approach the multilingual text classification task in the context of the epidemiological field.Multilingual text classification models tend to perform differently across different languages (low-or high-resource), more particularly when the dataset is highly imbalanced, which is the case for epidemiological datasets.We conduct a comparative study of different machine and deep learning text classification models using a dataset comprising news articles related to epidemic outbreaks from six languages, four low-resourced and two high-resourced, in order to analyze the influence of the nature of the language, the structure of the document, and the size of the data.Our findings indicate that the performance of the models based on fine-tuned language models exceeds by more than 50% the chosen baseline models that include a specialized epidemiological news surveillance system and several machine learning models.Also, low-resource languages are highly influenced not only by the typology of the languages on which the models have been pre-trained or/and fine-tuned but also by their size.Furthermore, we discover that the beginning and the end of documents provide the most salient features for this task and, as expected, the performance of the models was proportionate to the training data size.

Emanuela Boros, Ahmed Hamdi, Elvys Linhares Pontes, Luis Adrián Cabrera-Diego, Jose G. Moreno, Nicolas Sidere, Antoine Doucet. Proceedings of the 24th Conference on Computational Natural Language Learning. 2020.

This paper introduces a new corpus of multilingual medieval handwritten charter images, annotated with full transcription and named entities. The corpus is used to compare two approaches for named entity recognition in historical document images in several languages: on the one hand, a sequential approach, more commonly used, that sequentially applies handwritten text recognition (HTR) and named entity recognition (NER), on the other hand, a combined approach that simultaneously transcribes the image text line and extracts the entities. Experiments conducted on the charter corpus in Latin, early new high German and old Czech for name, date and location recognition demonstrate a superior performance of the combined approach.

In patrimonial institutions such as libraries and archives, the valorization of the vast amount of documents that have been recently digitized is still a challenge. Most of these documents are freely accessible as images but their textual content remains largely unreachable and unknown. Research projects dedicated to specific collection allow creating meta-data or even transcriptions obtained through volunteers or crowd-sourcing. But the vast majority of the documents cannot be manually transcribed or indexed: automatic large-scale processes for indexing are needed. The increasing adoption of the International Image Interoperability Framework (IIIF) by the patrimonial institutions is a technological enabler for the development of such services. Images are accessible with a unique protocol across institutions and both images and data can be presented with standard tools. In this paper, we describe an architecture for automatic processing of historical documents owned by different institutions but processed and presented thanks to the IIIF framework. We implemented this architecture and processed a large collection of books of hours with a page classifier trained on an annotated sample. The result is freely distributed and can be viewed with any IIIF compatible viewer.

Conference of 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014 ; Conference Date: 25 October 2014 Through 29 October 2014; Conference Code:111414