Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Hebrew
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
extended|other-reuters-corpus
ArXiv:
License:
other
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Splits for the Ben-Mordecai and Elhadad Hebrew NER Corpus (BMC)
In order to evaluate performance in accordance with the original Ben-Mordecai and Elhadad (2005) work, we provide three 75%-25% random splits.
- Only the 7 entity categories viable for evaluation were kept (DATE, LOC, MONEY, ORG, PER, PERCENT, TIME) --- all MISC entities were filtered out.
- Sequence label scheme was changed from IOB to BIOES
- The dev sets are 10% taken out of the 75%
Citation
If you use use the BMC corpus, please cite the original paper as well as our paper which describes the splits:
- Ben-Mordecai and Elhadad (2005):
@mastersthesis{naama,
title={Hebrew Named Entity Recognition},
author={Ben-Mordecai, Naama},
advisor={Elhadad, Michael},
year={2005},
url="https://www.cs.bgu.ac.il/~elhadad/nlpproj/naama/",
institution={Department of Computer Science, Ben-Gurion University},
school={Department of Computer Science, Ben-Gurion University},
}
- Bareket and Tsarfaty (2020)
@misc{bareket2020neural,
title={Neural Modeling for Named Entities and Morphology (NEMO^2)},
author={Dan Bareket and Reut Tsarfaty},
year={2020},
eprint={2007.15620},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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