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Dataset Open Access

HIPE-2022 Shared Task Named Entity Datasets

Ehrmann, Maud; Romanello, Matteo; Doucet, Antoine; Clematide, Simon

HIPE-2022 datasets used for the HIPE 2022 shared task on named entity recognition and classification (NERC) and entity linking (EL) in multilingual historical documents

HIPE-2022 datasets are based on six primary datasets assembled and prepared for the shared task. Primary datasets are composed of historical newspapers and classic commentaries covering ca. 200 years, feature several languages and different entity tag sets and annotation schemes. They originate from several European cultural heritage projects, from HIPE organizers’ previous research project, and from the previous HIPE-2020 campaign. Some are already published, others are released for the first time for HIPE-2022.

The HIPE-2022 shared task assembles and prepares these primary datasets in HIPE-2022 release(s), which correspond to a single package composed of neatly structured and homogeneously formatted files.

Primary datasets undergo the following preparation steps:

  • conversion to the HIPE format (with correction of data inconsistencies and metadata consolidation);
  • rearrangement or composition of train and dev splits.

Please also refer to:

Here is an overview of the primary datasets:

Dataset alias

Readme

Document type

Languages

Suitable for

Project

hipe2020

link

historical newspapers

de, fr, en

NERC-Coarse, NERC-Fine, EL

CLEF-HIPE-2020

newseye

link

historical newspapers

de, fi, fr, sv

NERC-Coarse, NERC-Fine, EL

NewsEye

sonar

link

historical newspapers

de

NERC-Coarse, EL

SoNAR

letemps

link

historical newspapers

fr

NERC-Coarse, NERC-Fine

LeTemps

topres19th

link

historical newspapers

en

NERC-Coarse, EL

Living with Machines

ajmc

link

classical commentaries

de, fr, en

NERC-Coarse, NERC-Fine, EL

AjMC

 

The HIPE-2022 team expresses her greatest appreciation to the partnering projects, namely AJMC, impresso, HIPE-2020, Living with Machines, NewsEye, and SoNAR, for contributing their NE-annotated datasets (and hiding a part thereof for the time of the evaluation campaign).

New releases are planned. Check the HIPE-2022 website for updates.
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