This dataset was created in the context of the CLEF-HIPE-2020 shared task. It consists of 563 historical newspaper articles annotated with 18,962 (linked) entity mentions (10,923 in French articles, 6,584 in German and 1,455 in English). This dataset can be used to train and benchmark NER and EL systems for historical texts.
For more information:
- HIPE-2020 data on Github and Zenodo
- CLEF-HIPE-2020 shared task website
- Participation guidelines
The CLEF-HIPE-2020 datasets are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Contact persons:
- Maud Ehrmann (EPFL)
- Matteo Romanello (UNIL)
- Simon Clematide (UZH)
Related publications
Extended Overview of CLEF HIPE 2020: Named Entity Processing on Historical Newspapers
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comparison of NE processing on historical texts, and, in the long run, fostering efficient semantic indexing of historical documents.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.
CLEF 2020 Working Notes. Conference and Labs of the Evaluation Forum
2020-10-21
11th Conference and Labs of the Evaluation Forum (CLEF 2020), [Online event], 22-25 September, 2020.DOI : 10.5281/zenodo.4117566
Impresso Named Entity Annotation Guidelines (CLEF-HIPE-2020)
Impresso annotation guidelines used in the context of corpus annotation for the HIPE shared task (CLEF 2020 Evaluation Lab). CLEF-HIPE-2020 shared task: https://impresso.github.io/CLEF-HIPE-2020/ Impresso project: https://impresso-project.ch
2020
p. 29.CLEF-HIPE-2020 – Shared Task Participation Guidelines
This document summarizes instructions for participants to the CLEF-HIPE-2020 shared task. HIPE (Identifying Historical People, Places and other Entities) is a named entity processing evaluation campaign on historical newspapers in French, German and English, organized in the context of the impresso project and run as a CLEF 2020 Evaluation Lab. More information on the website: https://impresso.github.io/CLEF-HIPE-2020/
2020
p. 19.Introducing the CLEF 2020 HIPE Shared Task: Named Entity Recognition and Linking on Historical Newspapers
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.
Advances in Information Retrieval. ECIR 2020
2020-04-08
ECIR 2020 : 42nd European Conference on Information Retrieval, Lisbon, Portugal, April 14-17, 2020.p. 524-532
DOI : 10.1007/978-3-030-45442-5_68