Language Resources for Historical Newspapers: the Impresso Collection

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 958–968 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 958 Language Resources for Historical Newspapers: the Impresso Collection Maud Ehrmann⋆, Matteo Romanello⋆, Simon Clematide†, Phillip Benjamin Str¨obel†, Rapha¨el Barman⋆ ⋆Digital Humanities Laboratory, EPFL †Institute for Computational Linguistics, Zurich University ⋆{maud.ehrmann, matteo.romanello, raphael.barman}@epfl.ch.ch †{siclemat, pstroebel}@cl.uzh.ch Abstract Following decades of massive digitization, an unprecedented amount of historical document facsimiles can now be retrieved and accessed via cultural heritage online portals. If this represents a huge step forward in terms of preservation and accessibility, the next fundamental challenge– and real promise of digitization– is to exploit the contents of these digital assets, and therefore to adapt and develop appropriate language technologies to search and retrieve information from this ‘Big Data of the Past’. Yet, the application of text processing tools on historical documents in general, and historical newspapers in particular, poses new challenges, and crucially requires appropriate language resources. In this context, this paper presents a collection of historical newspaper data sets composed of text and image resources, curated and published within the context of the ‘impresso - Media Monitoring of the Past’ project. With corpora, benchmarks, semantic annotations and language models in French, German and Luxembourgish covering ca. 200 years, the objective of the impresso resource collection is to contribute to historical language resources, and thereby strengthen the robustness of approaches to non-standard inputs and foster efficient processing of historical documents. Keywords: historical and multilingual language resources, historical texts, multi-layered historical semantic annotations, OCR, named entity processing, topic modeling, text reuse, digital humanities 1. Introduction Digitization efforts are slowly but steadily contributing an increasing amount of facsimiles of cultural heritage docu- ments. As a result, it is nowadays commonplace for many memory institutions to create and maintain digital reposito- ries which offer rapid, time- and location-independent ac- cess to documents (or surrogates thereof), allow to virtually bring together disperse collections, and ensure the preser- vation of fragile documents thanks to on-line consultation (Terras, 2011). Beyond this great achievement in terms of preservation and accessibility, the next fundamental chal- lenge –and real promise of digitization– is to exploit the contents of these digital assets, and therefore to adapt and develop appropriate language technologies to search and re- trieve information from this ‘Big Data of the Past’ (Kaplan and di Lenardo, 2017). In this regard, and following decisive grassroots efforts led by libraries to improve OCR (Optical Character Recogni- tion) technology and generalize full-text search over histor- ical document collections (see, e.g., the Impact1 and Trove2 projects), the Digital Humanities (DH), Natural Language Processing (NLP) and Computer Vision (CV) communities are pooling forces and expertise to push forward the pro- cessing of facsimiles, as well as the extraction, linking and representation of the complex information enclosed in tran- scriptions of digitized collections. These interdisciplinary efforts were recently streamlined within the far-reaching Europe Time Machine project3 which ambitions, in gen- eral, the application of artificial intelligence technologies …

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