task_categories:token-classification size_categories:100K<n<1M language:bn language:zh language:de language:en language:es language:fa language:fr language:hi language:it language:pt language:sv language:uk license:cc-by-4.0 multiconer ner multilingual named entity recognition fine-grained ner region:us
Complex named entities (NE), like the titles of creative works, are not simple nouns and pose challenges for NER systems (Ashwini and Choi, 2014). They can take the form of any linguistic constituent, like an imperative clause (“Dial M for Murder”), and do not look like traditional NEs (Persons, Locations, etc.). This syntactic ambiguity makes it challenging to recognize them based on context. We organized the MultiCoNER task (Malmasi et al., 2022) at SemEval-2022 to address these challenges in 11 languages, receiving a very positive community response with 34 system papers. Results confirmed the challenges of processing complex and long-tail NEs: even the largest pre-trained Transformers did not achieve top performance without external knowledge. The top systems infused transformers with knowledge bases and gazetteers. However, such solutions are brittle against out of knowledge-base entities and noisy scenarios like the presence of spelling mistakes and typos. We propose MultiCoNER II which represents novel challenges through new tasks that emphasize the shortcomings of the current top models.

MultiCoNER II features complex NER in these languages:

1. English
2. Spanish
3. Hindi
4. Bangla
5. Chinese
6. Swedish
7. Farsi
8. French
9. Italian
10. Portugese
11. Ukranian
12. German

For more details see https://multiconer.github.io/

## References
* Sandeep Ashwini and Jinho D. Choi. 2014. Targetable named entity recognition in social media. CoRR, abs/1408.0782.
* Shervin Malmasi, Anjie Fang, Besnik Fetahu, Sudipta Kar, Oleg Rokhlenko. 2022. SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER).
@inproceedings{multiconer2-report,
    title={{SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)}},
    author={Fetahu, Besnik and Kar, Sudipta and Chen, Zhiyu and Rokhlenko, Oleg and Malmasi, Shervin},
    booktitle={Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)},
    year={2023},
    publisher={Association for Computational Linguistics},
}

@article{multiconer2-data,
    title={{MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition}},
    author={Fetahu, Besnik and Chen, Zhiyu and Kar, Sudipta and Rokhlenko, Oleg and Malmasi, Shervin},
    year={2023},
}
---
license: cc-by-4.0
task_categories:
- token-classification
language:
- bn
- zh
- de
- en
- es
- fa
- fr
- hi
- it
- pt
- sv
- uk
tags:
- multiconer
- ner
- multilingual
- named entity recognition
- fine-grained ner
size_categories:
- 100K<n<1M
---
# Dataset Card for Multilingual Complex Named Entity Recognition (MultiCoNER)

## Dataset Description

- **Homepage:**  https://multiconer.github.io
- **Repository:** 
- **Paper:** 
- **Leaderboard:** https://multiconer.github.io/results, https://codalab.lisn.upsaclay.fr/competitions/10025
- **Point of Contact:** https://multiconer.github.io/organizers

### Dataset Summary

The tagset of MultiCoNER is a fine-grained tagset.
The fine to coarse level mapping of the tags are as follows:

* Location (LOC) : Facility, OtherLOC, HumanSettlement, Station
* Creative Work (CW) : VisualWork, MusicalWork, WrittenWork, ArtWork, Software
* Group (GRP) : MusicalGRP, PublicCORP, PrivateCORP, AerospaceManufacturer, SportsGRP, CarManufacturer, ORG
* Person (PER) : Scientist, Artist, Athlete, Politician, Cleric, SportsManager, OtherPER
* Product (PROD) : Clothing, Vehicle, Food, Drink, OtherPROD
* Medical (MED) : Medication/Vaccine, MedicalProcedure, AnatomicalStructure, Symptom, Disease



### Supported Tasks and Leaderboards

The final leaderboard of the shared task is available <a href="https://multiconer.github.io/results" target="_blank">here</a>.

### Languages
Supported languages are Bangla, Chinese, English, Spanish, Farsi, French, German, Hindi, Italian, Portuguese, Swedish, Ukrainian.

## Dataset Structure
The dataset follows CoNLL format.

### Data Instances

Here are some examples in different languages:

* Bangla: [লিটল মিক্স | MusicalGrp] এ যোগদানের আগে তিনি [পিৎজা হাট | ORG] এ ওয়েট্রেস হিসাবে কাজ করেছিলেন।
* Chinese: 它的纤维穿过 [锁骨 | AnatomicalStructure] 并沿颈部侧面倾斜向上和内侧.
* English: [wes anderson | Artist]'s film [the grand budapest hotel | VisualWork] opened the festival .
* Farsi:     است] ناگویا |HumanSettlement] مرکزاین استان شهر
* French: l [amiral de coligny | Politician] réussit à s y glisser .
* German: in [frühgeborenes | Disease] führt dies zu [irds | Symptom] .
* Hindi: १७९६ में उन्हें [शाही स्वीडिश विज्ञान अकादमी | Facility] का सदस्य चुना गया।
* Italian: è conservato nel [rijksmuseum | Facility] di [amsterdam | HumanSettlement] .
* Portuguese: também é utilizado para se fazer [licor | Drink] e [vinhos | Drink].
* Spanish: fue superado por el [aon center | Facility] de [los ángeles | HumanSettlement] .
* Swedish: [tom hamilton | Artist] amerikansk musiker basist i [aerosmith | MusicalGRP] .
* Ukrainian: назва альбому походить з роману « [кінець дитинства | WrittenWork] » англійського письменника [артура кларка | Artist] .

### Data Fields

The data has two fields. One is the token and another is the label. Here is an example from the English data.

```
# id f5458a3a-cd23-4df4-8384-4e23fe33a66b	domain=en
doris _ _ B-Artist
day _ _ I-Artist
included _ _ O
in _ _ O
the _ _ O
album _ _ O
billy _ _ B-MusicalWork
rose _ _ I-MusicalWork
's _ _ I-MusicalWork
jumbo _ _ I-MusicalWork
```


### Data Splits
Train, Dev, and Test splits are provided

## Dataset Creation
TBD

## Loading the Dataset
```python
from datasets import load_dataset

english_data = load_dataset('MultiCoNER/multiconer_v2', 'English (EN)')

```


### Licensing Information
CC BY 4.0

### Citation Information
```
@inproceedings{multiconer2-report,
    title={{SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)}},
    author={Fetahu, Besnik and Kar, Sudipta and Chen, Zhiyu and Rokhlenko, Oleg and Malmasi, Shervin},
    booktitle={Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)},
    year={2023},
    publisher={Association for Computational Linguistics},
}

@article{multiconer2-data,
    title={{MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition}},
    author={Fetahu, Besnik and Chen, Zhiyu and Kar, Sudipta and Rokhlenko, Oleg and Malmasi, Shervin},
    year={2023},
}
```

