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%

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} }