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Overview of CLEF HIPE 2020: Named Entity Recognition and Linking on Historical Newspapers


Ehrmann, Maud; Romanello, Matteo; Flückiger, Alex; Clematide, Simon (2020). Overview of CLEF HIPE 2020: Named Entity Recognition and Linking on Historical Newspapers. In: Arampatzis, Avi; Kanoulas, Evangelos; Tsikrika, Theodora; Vrochidis, Stefanos; Joho, Hideo; Lioma, Christina; Eickhoff, Carsten; Névéol, Aurélie; Cappellato, Linda; Ferro, Nicola. Experimental IR Meets Multilinguality, Multimodality, and Interaction. Cham: Springer, 288-310.

Abstract

This paper presents an overview of the first edition of HIPE (Identifying Historical People, Places and other Entities), a pioneering shared task dedicated to the evaluation of named entity processing on historical newspapers in French, German and English. Since its introduction some twenty years ago, named entity (NE) processing has become an essential component of virtually any text mining application and has undergone major changes. Recently, two main trends characterise its developments: the adoption of deep learning architectures and the consideration of textual material originating from historical and cultural heritage collections. While the former opens up new opportunities, the latter introduces new challenges with heterogeneous, historical and noisy inputs. In this context, the objective of HIPE, run as part of the CLEF 2020 conference, is threefold: strengthening the robustness of existing approaches on non-standard inputs, enabling performance comparison of NE processing on historical texts, and, in the long run, fostering efficient semantic indexing of historical documents. Tasks, corpora, and results of 13 participating teams are presented.

Abstract

This paper presents an overview of the first edition of HIPE (Identifying Historical People, Places and other Entities), a pioneering shared task dedicated to the evaluation of named entity processing on historical newspapers in French, German and English. Since its introduction some twenty years ago, named entity (NE) processing has become an essential component of virtually any text mining application and has undergone major changes. Recently, two main trends characterise its developments: the adoption of deep learning architectures and the consideration of textual material originating from historical and cultural heritage collections. While the former opens up new opportunities, the latter introduces new challenges with heterogeneous, historical and noisy inputs. In this context, the objective of HIPE, run as part of the CLEF 2020 conference, is threefold: strengthening the robustness of existing approaches on non-standard inputs, enabling performance comparison of NE processing on historical texts, and, in the long run, fostering efficient semantic indexing of historical documents. Tasks, corpora, and results of 13 participating teams are presented.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Scopus Subject Areas:Physical Sciences > Theoretical Computer Science
Physical Sciences > General Computer Science
Uncontrolled Keywords:Named Entity Recognition, NER, Named Entity Linking, NEL, Historical Newspaper
Language:English
Date:1 January 2020
Deposited On:15 Feb 2021 06:47
Last Modified:16 Feb 2021 21:01
Publisher:Springer
Series Name:Lecture Notes in Computer Science
Number:12260
ISSN:0302-9743
ISBN:9783030582180
OA Status:Closed
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1007/978-3-030-58219-7_21
Project Information:
  • : FunderSNSF
  • : Grant IDCRSII5_173719
  • : Project TitleMedia Monitoring of the Past

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