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Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents
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In: Advances in Information Retrieval. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II ; https://hal.archives-ouvertes.fr/hal-03635971 ; Matthias Hagen; Suzan Verberne; Craig Macdonald; Christin Seifert; Krisztian Balog; Kjetil Nørvåg; Vinay Setty. Advances in Information Retrieval. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II, 13186, Springer International Publishing, pp.347-354, 2022, Lecture Notes in Computer Science, 978-3-030-99738-0. ⟨10.1007/978-3-030-99739-7_44⟩ (2022)
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Grenzüberschreitendes Textmining von Historischen Zeitungen - Das impresso-Projekt zwischen Text- und Bildverarbeitung, Design und Geschichtswissenschaft ...
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Grenzüberschreitendes Textmining von Historischen Zeitungen - Das impresso-Projekt zwischen Text- und Bildverarbeitung, Design und Geschichtswissenschaft ...
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HIPE-2022 Shared Task Named Entity Datasets
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In: http://infoscience.epfl.ch/record/292174 (2022)
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Extended Overview of CLEF HIPE 2020: Named Entity Processing on Historical Newspapers
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In: http://infoscience.epfl.ch/record/281054 (2020)
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Introducing the CLEF 2020 HIPE Shared Task: Named Entity Recognition and Linking on Historical Newspapers
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In: http://infoscience.epfl.ch/record/277015 (2020)
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Overview of CLEF HIPE 2020: Named Entity Recognition and Linking on Historical Newspapers
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In: http://infoscience.epfl.ch/record/280047 (2020)
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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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URL: http://infoscience.epfl.ch/record/280047/files/HIPE_LNCS_CondensedLabOverview_postprint.pdf https://doi.org/10.1007/978-3-030-58219-7_21 https://link.springer.com/chapter/10.1007/978-3-030-58219-7_21 http://infoscience.epfl.ch/record/280047
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Introducing the CLEF 2020 HIPE Shared Task: Named Entity Recognition and Linking on Historical Newspapers
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Shared Task on Named Entity Recognition and Linking on Historical Newspapers ...
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Shared Task on Named Entity Recognition and Linking on Historical Newspapers ...
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Named Entity Processing for Historical Texts
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In: http://infoscience.epfl.ch/record/268328 (2019)
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A Selection of Papers from the Digital Classicist Seminar Berlin (2012-2015) ...
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Code Aus Einem Forschungskontext Mit Clam In Einen Web-Service Verwandeln ...
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The Ancient Greek Dependency Treebank: Linguistic Annotation in a Teaching Environment
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Mambrini, Francesco (orcid:0000-0003-0834-7562). - : Ubiquity Press, 2016. : country:GBR, 2016. : place:London, 2016
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