Datasets:
File size: 11,367 Bytes
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---
license: cc-by-4.0
task_categories:
- token-classification
language:
- bn
- de
- en
- es
- fa
- hi
- ko
- nl
- ru
- tr
- zh
- multilingual
tags:
- multiconer
- ner
- multilingual
- named entity recognition
size_categories:
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sequence: string
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---
# Multilingual Complex Named Entity Recognition (MultiCoNER)
## Dataset Summary
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
* `PER`: Person, i.e. names of people
* `LOC`: Location, i.e. locations/physical facilities
* `CORP`: Corporation, i.e. corporations/businesses
* `GRP`: Groups, i.e. all other groups
* `PROD`: Product, i.e. consumer products
* `CW`: Creative Work, i.e. movies/songs/book titles
### Dataset Structure
The dataset follows the IOB format of CoNLL. In particular, it uses the following label to ID mapping:
```python
{
"O": 0,
"B-PER": 1,
"I-PER": 2,
"B-LOC": 3,
"I-LOC": 4,
"B-CORP": 5,
"I-CORP": 6,
"B-GRP": 7,
"I-GRP": 8,
"B-PROD": 9,
"I-PROD": 10,
"B-CW": 11,
"I-CW": 12,
}
```
## Languages
The MultiCoNER dataset consists of the following languages: Bangla, German, English, Spanish, Farsi, Hindi, Korean, Dutch, Russian, Turkish and Chinese.
## Usage
```python
from datasets import load_dataset
dataset = load_dataset('tomaarsen/MultiCoNER', 'multi')
```
## License
CC BY 4.0
## Citation
```
@misc{malmasi2022multiconer,
title={MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition},
author={Shervin Malmasi and Anjie Fang and Besnik Fetahu and Sudipta Kar and Oleg Rokhlenko},
year={2022},
eprint={2208.14536},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |