HIPE-2022 Shared Task Named Entity Datasets
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https://zenodo.org/record/6089967
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资源简介：
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the HIPE 2022 shared task on named entity recognition and classification (NERC) and entity linking (EL) in multilingual historical documents.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:
HIPE-2022 shared task website: https://hipe-eval.github.io/HIPE-2022/
HIPE-2022 data repository: https://github.com/hipe-eval/HIPE-2022-data
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).
创建时间：
2022-05-25
