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