Datasets:
caner

Task Categories: structure-prediction
Languages: ar
Multilinguality: monolingual
Size Categories: 100K<n<1M
Language Creators: expert-generated
Annotations Creators: expert-generated
Source Datasets: original

Dataset Card for CANER

Dataset Summary

The Classical Arabic Named Entity Recognition corpus is a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities.

Supported Tasks and Leaderboards

  • Named Entity Recognition

Languages

Classical Arabic

Dataset Structure

Data Instances

An example from the dataset:

{'ner_tag': 1, 'token': 'الجامع'}

Where 1 stands for "Book"

Data Fields

  • id: id of the sample
  • token: the tokens of the example text
  • ner_tag: the NER tags of each token

The NER tags correspond to this list:

"Allah",
"Book",
"Clan",
"Crime",
"Date",
"Day",
"Hell",
"Loc",
"Meas",
"Mon",
"Month",
"NatOb",
"Number",
"O",
"Org",
"Para",
"Pers",
"Prophet",
"Rlig",
"Sect",
"Time"

Data Splits

Training splits only

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

Ramzi Salah and Lailatul Qadri Zakaria

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

[More Information Needed]

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@article{article, author = {Salah, Ramzi and Zakaria, Lailatul}, year = {2018}, month = {12}, pages = {}, title = {BUILDING THE CLASSICAL ARABIC NAMED ENTITY RECOGNITION CORPUS (CANERCORPUS)}, volume = {96}, journal = {Journal of Theoretical and Applied Information Technology} }

Contributions

Thanks to @KMFODA for adding this dataset.

Models trained or fine-tuned on caner

None yet