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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
- source and target: a pair of entities identified in the text
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.
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