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--- |
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license: cc-by-4.0 |
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task_categories: |
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- token-classification |
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language: |
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- bn |
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- de |
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- en |
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- es |
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- fa |
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- hi |
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- ko |
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- nl |
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- ru |
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- tr |
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- zh |
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- multilingual |
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tags: |
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- multiconer |
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- ner |
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- multilingual |
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- named entity recognition |
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size_categories: |
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- 100K<n<1M |
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dataset_info: |
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- config_name: bn |
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features: |
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- name: id |
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dtype: int32 |
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sequence: string |
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'5': B-CORP |
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'6': I-CORP |
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'7': B-GRP |
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'9': B-PROD |
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'11': B-CW |
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'12': I-CW |
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download_size: 31446032 |
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--- |
|
|
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# Multilingual Complex Named Entity Recognition (MultiCoNER) |
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|
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## Dataset Summary |
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MultiCoNER (version 1) is a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. This dataset is designed to represent contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities like movie titles, and long-tail entity distributions. The 26M token dataset is compiled from public resources using techniques such as heuristic-based sentence sampling, template extraction and slotting, and machine translation. |
|
|
|
See the [AWS Open Data Registry entry for MultiCoNER](https://registry.opendata.aws/multiconer/) for more information. |
|
|
|
## Labels |
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* `PER`: Person, i.e. names of people |
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* `LOC`: Location, i.e. locations/physical facilities |
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* `CORP`: Corporation, i.e. corporations/businesses |
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* `GRP`: Groups, i.e. all other groups |
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* `PROD`: Product, i.e. consumer products |
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* `CW`: Creative Work, i.e. movies/songs/book titles |
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|
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### Dataset Structure |
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The dataset follows the IOB format of CoNLL. In particular, it uses the following label to ID mapping: |
|
```python |
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|
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{ |
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"O": 0, |
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"B-PER": 1, |
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"I-PER": 2, |
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"B-LOC": 3, |
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"I-LOC": 4, |
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"B-CORP": 5, |
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"I-CORP": 6, |
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"B-GRP": 7, |
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"I-GRP": 8, |
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"B-PROD": 9, |
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"I-PROD": 10, |
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"B-CW": 11, |
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"I-CW": 12, |
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} |
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``` |
|
|
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## Languages |
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The MultiCoNER dataset consists of the following languages: Bangla, German, English, Spanish, Farsi, Hindi, Korean, Dutch, Russian, Turkish and Chinese. |
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|
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## Usage |
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```python |
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from datasets import load_dataset |
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|
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dataset = load_dataset('tomaarsen/MultiCoNER', 'multi') |
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``` |
|
|
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## License |
|
|
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CC BY 4.0 |
|
|
|
## Citation |
|
``` |
|
@misc{malmasi2022multiconer, |
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title={MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition}, |
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author={Shervin Malmasi and Anjie Fang and Besnik Fetahu and Sudipta Kar and Oleg Rokhlenko}, |
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year={2022}, |
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eprint={2208.14536}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
|
``` |
