# 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]
task_categories:token-classification language:bg license:apache-2.0 region:us
{"language": ["bg"], "license": "apache-2.0", "task_categories": ["token-classification"]}
TAGS
#task_categories-token-classification #language-Bulgarian #license-apache-2.0 #region-us 

# 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



### Languages



## Dataset Structure

### Data Instances



### Data Fields



### Data Splits

train, test

## Dataset Creation

### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators



### Licensing 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 = "URL
   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 = "URL
    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


# 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 ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits

train, test ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing 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 = "URL
   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 = "URL
    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
TAGS
#task_categories-token-classification #language-Bulgarian #license-apache-2.0 #region-us 
 # 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 ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits

train, test ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing 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 = "URL
   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 = "URL
    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
passage: TAGS
#task_categories-token-classification #language-Bulgarian #license-apache-2.0 #region-us 
# 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### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits

train, test## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators