Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
Hebrew
Size:
1K - 10K
ArXiv:
License:
File size: 1,838 Bytes
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---
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
---
# 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}
}
```
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