

# 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):
```console
@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)
```console
@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}
}
```


task_categories:token-classification task_ids:named-entity-recognition annotations_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:10K<n<100K source_datasets:extended|other-reuters-corpus language:he license:other arxiv:2007.15620 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["he"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|other-reuters-corpus"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "train-eval-index": [{"config": "bmc", "task": "token-classification", "task_id": "entity_extraction", "splits": {"train_split": "train", "eval_split": "validation", "test_split": "test"}, "col_mapping": {"tokens": "tokens", "ner_tags": "tags"}, "metrics": [{"type": "seqeval", "name": "seqeval"}]}]}
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other-reuters-corpus #language-Hebrew #license-other #arxiv-2007.15620 #region-us 



# 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%


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):


* Bareket and Tsarfaty (2020)



# 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%


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):


* Bareket and Tsarfaty (2020)
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other-reuters-corpus #language-Hebrew #license-other #arxiv-2007.15620 #region-us 
 # 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%


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):


* Bareket and Tsarfaty (2020)
passage: TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other-reuters-corpus #language-Hebrew #license-other #arxiv-2007.15620 #region-us 
# 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%


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):


* Bareket and Tsarfaty (2020)