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--- |
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license: apache-2.0 |
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task_categories: |
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- token-classification |
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language: |
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- bg |
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--- |
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# 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. |
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## Dataset Description |
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- **Homepage:** |
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- **Repository:** |
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- **Paper:** |
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- **Leaderboard:** |
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- **Point of Contact:** |
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### Dataset Summary |
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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" |
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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. |
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### Supported Tasks and Leaderboards |
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[More Information Needed] |
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### Languages |
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[More Information Needed] |
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## Dataset Structure |
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### Data Instances |
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[More Information Needed] |
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### Data Fields |
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[More Information Needed] |
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### Data Splits |
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train, test |
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## Dataset Creation |
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### Curation Rationale |
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[More Information Needed] |
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### Source Data |
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#### Initial Data Collection and Normalization |
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[More Information Needed] |
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#### Who are the source language producers? |
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[More Information Needed] |
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### Annotations |
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#### Annotation process |
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[More Information Needed] |
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#### Who are the annotators? |
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[More Information Needed] |
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### Personal and Sensitive Information |
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[More Information Needed] |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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[More Information Needed] |
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### Discussion of Biases |
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[More Information Needed] |
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### Other Known Limitations |
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[More Information Needed] |
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## Additional Information |
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### Dataset Curators |
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[More Information Needed] |
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### Licensing Information |
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[More Information Needed] |
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### Citation Information |
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@inproceedings{piskorski-etal-2019-second, |
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title = "The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across {S}lavic Languages", |
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author = "Piskorski, Jakub and Laskova, Laska and Marci{\'n}czuk, Micha{\l} and Pivovarova, Lidia and P{\v{r}}ib{\'a}{\v{n}}, Pavel |
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and Steinberger, Josef and Yangarber, Roman", |
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booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing", |
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month = aug, |
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year = "2019", |
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address = "Florence, Italy", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/W19-3709", |
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pages = "63--74" |
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} |
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@inproceedings{marinova-etal-2020-reconstructing, |
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title = "Reconstructing {NER} Corpora: a Case Study on {B}ulgarian", |
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author = "Marinova, Iva and |
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Laskova, Laska and |
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Osenova, Petya and |
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Simov, Kiril and |
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Popov, Alexander", |
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booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference", |
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month = may, |
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year = "2020", |
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address = "Marseille, France", |
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publisher = "European Language Resources Association", |
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url = "https://aclanthology.org/2020.lrec-1.571", |
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pages = "4647--4652", |
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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.", |
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language = "English", |
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ISBN = "979-10-95546-34-4", |
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} |
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### Contributions |
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[More Information Needed] |
