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Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents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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Explorer la presse numérisée : le projet Impresso
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In: http://infoscience.epfl.ch/record/292173 (2022)
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Historical Newspaper Content Mining: findings from the impresso project ...
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Historical Newspaper Content Mining: findings from the impresso project ...
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More Data and New Tools. Advances in Parsing the Index Thomisticus Treebank
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Deep-diving in NLP enhanced digitised newspapers. A hands-on session with the impresso interface ...
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Deep-diving in NLP enhanced digitised newspapers. A hands-on session with the impresso interface ...
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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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Abstract:
This paper presents an extended 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. Compared to the condensed overview [31], this paper includes further details about data generation and statistics, additional information on participating systems, and the presentation of complementary results.
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URL: https://zenodo.org/record/3604227 https://infoscience.epfl.ch/record/281054/files/paper_255-2.pdf https://doi.org/10.5281/zenodo.4117566 https://zenodo.org/record/3677171 http://ceur-ws.org/Vol-2696/ http://infoscience.epfl.ch/record/281054 https://impresso.github.io/CLEF-HIPE-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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Introducing the CLEF 2020 HIPE Shared Task: Named Entity Recognition and Linking on Historical Newspapers
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Representation and parsing of multiword expressions: Current trends
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In: Language Science Press; (2019)
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Representation and parsing of multiword expressions: Current trends
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In: Language Science Press; (2019)
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