Finding Nineteenth-century Berry Spots: Recognizing and Linking Place Names in a Historical Newspaper Berry-picking Corpus

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationDHN 2019 - Digital Humanities in the Nordic Countries
Subtitle of host publicationProceedings of the Digital Humanities in the Nordic Countries 4th Conference, Copenhagen, Denmark, March 5-8, 2019
EditorsCostanza Navarretta, Manex Agirrezabal, Bente Maegaard
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventDigital Humanities in the Nordic Countries - University of Copenhagen, Copenhagen, Denmark
Duration: 6 Mar 20198 Mar 2019
Conference number: 4
https://cst.dk/DHN2019/DHN2019.html

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR
Volume2364
ISSN (Print)1613-0073
ISSN (Electronic)1613-0073

Conference

ConferenceDigital Humanities in the Nordic Countries
Abbreviated titleDHN
CountryDenmark
CityCopenhagen
Period06/03/201908/03/2019
Internet address

Researchers

Research units

  • The National Library of Finland

Abstract

The paper studies and improves methods of named entity recognition (NER) and linking (NEL) for facilitating historical research, which uses digitized newspaper texts. The specific focus is on a study about historical process of commodification. The named entity detection pipeline is discussed in three steps. First, the paper presents the corpus, which consists of newspaper articles on wild berry picking from the late nineteenth century. Second, the paper compares two named entity recognition tools: the trainable Stanford NER and the rule-based FiNER. Third, the linking and disambiguation of the recognized places is explored. In the linking process, information about the newspaper publication place is used to improve the identification of small places.
The paper concludes that the pipeline performs well for mapping the commodification, and that specific problems relate to the recognition of place names (among named entities). It is shown how Stanford NER performs better in the task (F-score of 0.83) than the FiNER tool (F-score of 0.68). Concerning the linking of places, the use of newspaper metadata appears useful for disambiguation between small places. However, the historical language (with its OCR errors) recognized by the Stanford model poses challenges for the linking tool. The paper proposes that other information, for instance about the reuse of the newspaper articles, could be used to further improve the recognition and linking quality.

ID: 32437310