Datasets:
Tasks:
Text Retrieval
Sub-tasks:
entity-linking-retrieval
Languages:
Polish
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
License:
metadata
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- pl
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- entity-linking-retrieval
pretty_name: bprec
dataset_info:
- config_name: default
features:
- name: id
dtype: int32
- name: text
dtype: string
- name: ner
sequence:
- name: source
struct:
- name: from
dtype: int32
- name: text
dtype: string
- name: to
dtype: int32
- name: type
dtype:
class_label:
names:
'0': PRODUCT_NAME
'1': PRODUCT_NAME_IMP
'2': PRODUCT_NO_BRAND
'3': BRAND_NAME
'4': BRAND_NAME_IMP
'5': VERSION
'6': PRODUCT_ADJ
'7': BRAND_ADJ
'8': LOCATION
'9': LOCATION_IMP
- name: target
struct:
- name: from
dtype: int32
- name: text
dtype: string
- name: to
dtype: int32
- name: type
dtype:
class_label:
names:
'0': PRODUCT_NAME
'1': PRODUCT_NAME_IMP
'2': PRODUCT_NO_BRAND
'3': BRAND_NAME
'4': BRAND_NAME_IMP
'5': VERSION
'6': PRODUCT_ADJ
'7': BRAND_ADJ
'8': LOCATION
'9': LOCATION_IMP
splits:
- name: tele
num_bytes: 2739015
num_examples: 2391
- name: electro
num_bytes: 125999
num_examples: 382
- name: cosmetics
num_bytes: 1565263
num_examples: 2384
- name: banking
num_bytes: 446944
num_examples: 561
download_size: 8006167
dataset_size: 4877221
- config_name: all
features:
- name: id
dtype: int32
- name: category
dtype: string
- name: text
dtype: string
- name: ner
sequence:
- name: source
struct:
- name: from
dtype: int32
- name: text
dtype: string
- name: to
dtype: int32
- name: type
dtype:
class_label:
names:
'0': PRODUCT_NAME
'1': PRODUCT_NAME_IMP
'2': PRODUCT_NO_BRAND
'3': BRAND_NAME
'4': BRAND_NAME_IMP
'5': VERSION
'6': PRODUCT_ADJ
'7': BRAND_ADJ
'8': LOCATION
'9': LOCATION_IMP
- name: target
struct:
- name: from
dtype: int32
- name: text
dtype: string
- name: to
dtype: int32
- name: type
dtype:
class_label:
names:
'0': PRODUCT_NAME
'1': PRODUCT_NAME_IMP
'2': PRODUCT_NO_BRAND
'3': BRAND_NAME
'4': BRAND_NAME_IMP
'5': VERSION
'6': PRODUCT_ADJ
'7': BRAND_ADJ
'8': LOCATION
'9': LOCATION_IMP
splits:
- name: train
num_bytes: 4937658
num_examples: 5718
download_size: 8006167
dataset_size: 4937658
- config_name: tele
features:
- name: id
dtype: int32
- name: category
dtype: string
- name: text
dtype: string
- name: ner
sequence:
- name: source
struct:
- name: from
dtype: int32
- name: text
dtype: string
- name: to
dtype: int32
- name: type
dtype:
class_label:
names:
'0': PRODUCT_NAME
'1': PRODUCT_NAME_IMP
'2': PRODUCT_NO_BRAND
'3': BRAND_NAME
'4': BRAND_NAME_IMP
'5': VERSION
'6': PRODUCT_ADJ
'7': BRAND_ADJ
'8': LOCATION
'9': LOCATION_IMP
- name: target
struct:
- name: from
dtype: int32
- name: text
dtype: string
- name: to
dtype: int32
- name: type
dtype:
class_label:
names:
'0': PRODUCT_NAME
'1': PRODUCT_NAME_IMP
'2': PRODUCT_NO_BRAND
'3': BRAND_NAME
'4': BRAND_NAME_IMP
'5': VERSION
'6': PRODUCT_ADJ
'7': BRAND_ADJ
'8': LOCATION
'9': LOCATION_IMP
splits:
- name: train
num_bytes: 2758147
num_examples: 2391
download_size: 4569708
dataset_size: 2758147
- config_name: electro
features:
- name: id
dtype: int32
- name: category
dtype: string
- name: text
dtype: string
- name: ner
sequence:
- name: source
struct:
- name: from
dtype: int32
- name: text
dtype: string
- name: to
dtype: int32
- name: type
dtype:
class_label:
names:
'0': PRODUCT_NAME
'1': PRODUCT_NAME_IMP
'2': PRODUCT_NO_BRAND
'3': BRAND_NAME
'4': BRAND_NAME_IMP
'5': VERSION
'6': PRODUCT_ADJ
'7': BRAND_ADJ
'8': LOCATION
'9': LOCATION_IMP
- name: target
struct:
- name: from
dtype: int32
- name: text
dtype: string
- name: to
dtype: int32
- name: type
dtype:
class_label:
names:
'0': PRODUCT_NAME
'1': PRODUCT_NAME_IMP
'2': PRODUCT_NO_BRAND
'3': BRAND_NAME
'4': BRAND_NAME_IMP
'5': VERSION
'6': PRODUCT_ADJ
'7': BRAND_ADJ
'8': LOCATION
'9': LOCATION_IMP
splits:
- name: train
num_bytes: 130205
num_examples: 382
download_size: 269917
dataset_size: 130205
- config_name: cosmetics
features:
- name: id
dtype: int32
- name: category
dtype: string
- name: text
dtype: string
- name: ner
sequence:
- name: source
struct:
- name: from
dtype: int32
- name: text
dtype: string
- name: to
dtype: int32
- name: type
dtype:
class_label:
names:
'0': PRODUCT_NAME
'1': PRODUCT_NAME_IMP
'2': PRODUCT_NO_BRAND
'3': BRAND_NAME
'4': BRAND_NAME_IMP
'5': VERSION
'6': PRODUCT_ADJ
'7': BRAND_ADJ
'8': LOCATION
'9': LOCATION_IMP
- name: target
struct:
- name: from
dtype: int32
- name: text
dtype: string
- name: to
dtype: int32
- name: type
dtype:
class_label:
names:
'0': PRODUCT_NAME
'1': PRODUCT_NAME_IMP
'2': PRODUCT_NO_BRAND
'3': BRAND_NAME
'4': BRAND_NAME_IMP
'5': VERSION
'6': PRODUCT_ADJ
'7': BRAND_ADJ
'8': LOCATION
'9': LOCATION_IMP
splits:
- name: train
num_bytes: 1596259
num_examples: 2384
download_size: 2417388
dataset_size: 1596259
- config_name: banking
features:
- name: id
dtype: int32
- name: category
dtype: string
- name: text
dtype: string
- name: ner
sequence:
- name: source
struct:
- name: from
dtype: int32
- name: text
dtype: string
- name: to
dtype: int32
- name: type
dtype:
class_label:
names:
'0': PRODUCT_NAME
'1': PRODUCT_NAME_IMP
'2': PRODUCT_NO_BRAND
'3': BRAND_NAME
'4': BRAND_NAME_IMP
'5': VERSION
'6': PRODUCT_ADJ
'7': BRAND_ADJ
'8': LOCATION
'9': LOCATION_IMP
- name: target
struct:
- name: from
dtype: int32
- name: text
dtype: string
- name: to
dtype: int32
- name: type
dtype:
class_label:
names:
'0': PRODUCT_NAME
'1': PRODUCT_NAME_IMP
'2': PRODUCT_NO_BRAND
'3': BRAND_NAME
'4': BRAND_NAME_IMP
'5': VERSION
'6': PRODUCT_ADJ
'7': BRAND_ADJ
'8': LOCATION
'9': LOCATION_IMP
splits:
- name: train
num_bytes: 453119
num_examples: 561
download_size: 749154
dataset_size: 453119
Dataset Card for [Dataset Name]
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: bprec homepage
- Repository: bprec repository
- Paper: bprec paper
- Leaderboard:
- Point of Contact:
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.