Person-Centric Mining of Historical Newspaper Collections

Ardanuy, Mariona Coll; Knauth, Jürgen; Beliankou, Andrei; Bos, Maarten van den; Sporleder, Caroline

doi:10.1007/978-3-319-43997-6_25

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…And finally, content-driven access might help widening the interested user base, as it provides a lower barrier to entry than a contextual approach. Our work in this respect is in line with the efforts to build entity-centric information retrieval systems for historical contents (Boschetti et al, 2014;Coll Ardanuy et al, 2016;Menini et al, 2017). Overall, this methodology takes into account both the long-term goal of content-driven access and the short-term constraints of resources (costs) and technology (current limits to automation, especially OCR of handwritten documents).…”

Section: Approachmentioning

confidence: 70%

Index-Driven Digitization and Indexation of Historical Archives

Colavizza

Ehrmann

Bortoluzzi

2019

Front. Digit. Humanit.

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The promise of digitization of historical archives lies in their indexation at the level of contents. Unfortunately, this kind of indexation does not scale, if done manually. In this article we present a method to bootstrap the deployment of a content-based information system for digitized historical archives, relying on historical indexing tools. Commonly prepared to search within homogeneous records when the archive was still current, such indexes were as widespread as they were disconnected, that is to say situated in the very records they were meant to index. We first present a conceptual model to describe and manipulate historical indexing tools. We then introduce a methodological framework for their use in order to guide digitization campaigns and index digitized historical records. Finally, we exemplify the approach with a case study on the indexation system of the X Savi alle Decime in Rialto, a Venetian magistracy in charge for the exaction-and related record keeping-of a tax on real estate in early modern Venice.

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Section: Approachmentioning

confidence: 70%

Index-Driven Digitization and Indexation of Historical Archives

Colavizza

Ehrmann

Bortoluzzi

2019

Front. Digit. Humanit.

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“…4 http://wiki.dbpedia.org/Downloads 5. Interlanguage links from version to version of Wikipedia can be downloaded from the Wikipedia dumps: http://dumps.wikimedia.org/enwiki/latest/enwiki-latest-langlinks.sql.gz.…”

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confidence: 99%

Entity-Centric Text Mining for Historical Documents

Ardanuy¹

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Recent years have seen an important increase of digitization projects in the cultural heritage domain. As a result, growing efforts have been directed towards the study of natural language processing technologies that support research in the humanities. This thesis is a contribution to the study and development of new text mining strategies that allow a better exploration of contemporary history collections from an entity-centric perspective. In particular, this thesis focuses on the challenging problems of disambiguating two specific kinds of named entities: toponyms and person names. They are approached as two clearly differentiated tasks, each of which exploiting the inherent characteristics that are associated to each kind of named entity.Finding the correct referent of a toponym is a challenging task, and this difficulty is even more pronounced in the historical domain, as it is not uncommon that places change their names over time. The method proposed in this thesis to disambiguate toponyms, GeoSem, is especially suited to work with collections of historical texts. It is a weakly-supervised model that combines the strengths of both toponym resolution and entity linking approaches by exploiting both geographic and semantic features. In order to do so, the method makes use of a knowledge base built using Wikipedia as a basis and complemented with additional knowledge from GeoNames.The method has been tested on a historical toponym resolution benchmark dataset in English and improved on the state of the art. Furthermore, five datasets of historical news in German and Dutch have been created from scratch and annotated. The method proposed in this thesis performs significantly better on them than two out-of-the-box state-of-the-art entity linking methods when only locations are considered for evaluation. This dissertation would not have been completed without the support and encouragement of many people. First and foremost, I wish to thank my supervisor, Professor Caroline Sporleder, from whom I have learned much, for her trust, dedicated support and guidance during these years. I would also like to thank Professor Ramin Yahyapour, for accepting to be part of this thesis committee and sharing with me some insightful comments to improve the structure of this dissertation. I would also like to gratefully acknowledge Professors Ulrich Heid, Dieter Hogrefe, Gerhard Lauer, and Wolfgang May, for their willingness to serve on my examination committee.

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Person-Centric Mining of Historical Newspaper Collections

Cited by 2 publications

References 17 publications

Index-Driven Digitization and Indexation of Historical Archives

Index-Driven Digitization and Indexation of Historical Archives

Entity-Centric Text Mining for Historical Documents

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