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

Maud Ehrmann, Matteo Romanello
Springer, 2020
Article de conférence

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comparison of NE processing on historical texts, and, in the long run, fostering efficient semantic indexing of historical documents.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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Maud Ehrmann, Matteo Romanello
Springer, 2020
Article de conférence

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https://infoscience.epfl.ch/record/280047?ln=fr

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Introducing the CLEF 2020 HIPE Shared Task: Named Entity Recognition and Linking on Historical Newspapers

Maud Ehrmann, Matteo Romanello

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. If NE processing tools are increasingly being used in the context of historical documents, performance values are below the ones on contemporary data and are hardly comparable. In this context, this paper introduces the CLEF 2020 Evaluation Lab HIPE (Identifying Historical People, Places and other Entities) on named entity recognition and linking on diachronic historical newspaper material in French, German and English. Our objective 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 in order to support scholarship on digital cultural heritage collections.

Springer International Publishing2020

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Extended Overview of CLEF HIPE 2020: Named Entity Processing on Historical Newspapers

Maud Ehrmann, Matteo Romanello

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.

CEUR-WS2020

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Recognition and identification of real-world entities is at the core of virtually any text mining application. As a matter of fact, referential units such as names of persons, locations and organizations underlie the semantics of texts and guide their interpretation. Around since the seminal Message Understanding Conference (MUC) evaluation cycle in the 1990s, named entity-related tasks have undergone major evolutions until now, from entity recognition and classification to entity disambiguation and linking. Recently, NE processing has been called upon to contribute to the domain of digital humanities, where massive digitization of historical documents is producing huge amounts of texts. De facto, NE processing tools are increasingly being used in the context of historical documents. Research activities in this domain target texts of different nature (e.g., publications by cultural institutions, state-related documents, genealogical data, historical newspapers) and different tasks (NE recognition and classification, entity linking, or both). Experiments involve different time periods (from 16th to 20th c.), focus on different domains, and use different typologies. This great variety demonstrates how many and varied the needs – and the challenges – are, but makes performance comparison difﬁcult, not to say impossible. The objective of this tutorial is to provide the participants with essential knowledge with respect to a) NE processing in general and in DH, and b) how to apply NE recognition approaches.

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