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Dataset Summary

Brand-Product Relation Extraction Corpora in Polish

Supported Tasks and Leaderboards

NER, Entity linking

Languages

Polish

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

• id: int identifier of a text
• text: string text, for example a consumer comment on the social media
• ner: extracted entities and their relationship
  • source and target: a pair of entities identified in the text
    • from: int value representing starting character of the entity
    • text: string value with the entity text
    • to: int value representing end character of the entity
    • type: one of pre-identified entity types:
      • PRODUCT_NAME
      • PRODUCT_NAME_IMP
      • PRODUCT_NO_BRAND
      • BRAND_NAME
      • BRAND_NAME_IMP
      • VERSION
      • PRODUCT_ADJ
      • BRAND_ADJ
      • LOCATION
      • LOCATION_IMP

Data Splits

No train/validation/test split provided. Current dataset configurations point to 4 domain categories for the texts:

• tele
• electro
• cosmetics
• banking

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{inproceedings,
author = {Janz, Arkadiusz and Kopociński, Łukasz and Piasecki, Maciej and Pluwak, Agnieszka},
year = {2020},
month = {05},
pages = {},
title = {Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations}
}

Contributions

Thanks to @kldarek for adding this dataset.