clarin-pl/bprec

---
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-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)

## Dataset Description

- **Homepage:** [bprec homepage](https://clarin-pl.eu/dspace/handle/11321/736)
- **Repository:** [bprec repository](https://gitlab.clarin-pl.eu/team-semantics/semrel-extraction)
- **Paper:** [bprec paper](https://www.aclweb.org/anthology/2020.lrec-1.233.pdf)
- **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

### 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](https://github.com/kldarek) for adding this dataset.

原始信息汇总

数据集概述

基本信息

• 名称: bprec
• 语言: 波兰语 (pl)
• 许可证: 未知
• 多语言性: 单语种
• 数据集大小: 1K<n<10K
• 数据来源: 原始数据
• 任务类别: 文本检索
• 任务ID: 实体链接检索

数据集结构

• 配置名称: default
  • 特征:
    • id: 整数类型，文本标识符
    • text: 字符串类型，例如社交媒体上的消费者评论
    • ner: 提取的实体及其关系
      • source 和 target: 文本中识别的一对实体
        • from: 整数类型，实体起始字符位置
        • text: 字符串类型，实体文本
        • to: 整数类型，实体结束字符位置
        • type: 预定义的实体类型之一
          • PRODUCT_NAME
          • PRODUCT_NAME_IMP
          • PRODUCT_NO_BRAND
          • BRAND_NAME
          • BRAND_NAME_IMP
          • VERSION
          • PRODUCT_ADJ
          • BRAND_ADJ
          • LOCATION
          • LOCATION_IMP
  • 数据分割:
    • tele: 2391个示例，2739015字节
    • electro: 382个示例，125999字节
    • cosmetics: 2384个示例，1565263字节
    • banking: 561个示例，446944字节

数据集创建

• 注释创建者: 专家生成
• 语言创建者: 专家生成

数据集使用注意事项

• 数据分割: 未提供训练/验证/测试分割，当前数据集配置指向4个领域类别
• 下载大小: 8006167字节
• 数据集大小: 4877221字节