task_categories:token-classification language:bg license:apache-2.0 region:us
---
license: apache-2.0
task_categories:
- token-classification
language:
- bg
---
# Dataset Card for Bulgarian Named Entity Recognition. Initial dataset is taken from Balto-Slavic NLP shared task and is further transformed in the format appropriate for token classification. The instances are randomized and splitted into train and test splits.

## Dataset Description

- **Homepage:** 
- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

This dataset is initially created for the BSNLP Shared Task 2019 and reported in the conference paper "The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages"
It is further improved in "Reconstructing NER Corpora: a Case Study on Bulgarian" and finally transformed in a csv format appropriate for token classification in Huggingface.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

[More Information Needed]

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Data Splits

train, test

## 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?

[More Information Needed]

### 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

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

@inproceedings{piskorski-etal-2019-second,
   title = "The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across {S}lavic Languages",
   author = "Piskorski, Jakub  and Laskova, Laska  and Marci{\'n}czuk, Micha{\l}  and Pivovarova, Lidia  and P{\v{r}}ib{\'a}{\v{n}}, Pavel  
   and Steinberger, Josef  and Yangarber, Roman",
   booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
   month = aug,
   year = "2019",
   address = "Florence, Italy",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/W19-3709",
   pages = "63--74"
   }

   @inproceedings{marinova-etal-2020-reconstructing,
    title = "Reconstructing {NER} Corpora: a Case Study on {B}ulgarian",
    author = "Marinova, Iva  and
      Laskova, Laska  and
      Osenova, Petya  and
      Simov, Kiril  and
      Popov, Alexander",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2020.lrec-1.571",
    pages = "4647--4652",
    abstract = "The paper reports on the usage of deep learning methods for improving a Named Entity Recognition (NER) training corpus and for predicting and annotating new types in a test corpus. We show how the annotations in a type-based corpus of named entities (NE) were populated as occurrences within it, thus ensuring density of the training information. A deep learning model was adopted for discovering inconsistencies in the initial annotation and for learning new NE types. The evaluation results get improved after data curation, randomization and deduplication.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}


### Contributions

[More Information Needed]