jerbarnes/multi_booked

---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ca
- eu
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: multibooked
pretty_name: MultiBooked
dataset_info:
- config_name: ca
features:
- name: text
sequence:
- name: wid
dtype: string
- name: sent
dtype: string
- name: para
dtype: string
- name: word
dtype: string
- name: terms
sequence:
- name: tid
dtype: string
- name: lemma
dtype: string
- name: morphofeat
dtype: string
- name: pos
dtype: string
- name: target
sequence: string
- name: opinions
sequence:
- name: oid
dtype: string
- name: opinion_holder_target
sequence: string
- name: opinion_target_target
sequence: string
- name: opinion_expression_polarity
dtype:
class_label:
names:
'0': StrongNegative
'1': Negative
'2': Positive
'3': StrongPositive
- name: opinion_expression_target
sequence: string
splits:
- name: train
num_bytes: 1952731
num_examples: 567
download_size: 4429415
dataset_size: 1952731
- config_name: eu
features:
- name: text
sequence:
- name: wid
dtype: string
- name: sent
dtype: string
- name: para
dtype: string
- name: word
dtype: string
- name: terms
sequence:
- name: tid
dtype: string
- name: lemma
dtype: string
- name: morphofeat
dtype: string
- name: pos
dtype: string
- name: target
sequence: string
- name: opinions
sequence:
- name: oid
dtype: string
- name: opinion_holder_target
sequence: string
- name: opinion_target_target
sequence: string
- name: opinion_expression_polarity
dtype:
class_label:
names:
'0': StrongNegative
'1': Negative
'2': Positive
'3': StrongPositive
- name: opinion_expression_target
sequence: string
splits:
- name: train
num_bytes: 1175816
num_examples: 343
download_size: 4429415
dataset_size: 1175816
config_names:
- ca
- eu
---

# Dataset Card for MultiBooked

## 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:** http://hdl.handle.net/10230/33928
- **Repository:** https://github.com/jerbarnes/multibooked
- **Paper:** https://arxiv.org/abs/1803.08614
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.

The corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is
an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and
word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two
annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the
guidelines set out in the OpeNER project.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

Each sub-dataset is monolingual in the languages:
- ca: Catalan
- eu: Basque

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

- `text`: layer of the original text.
- `wid`: list of word IDs for each word within the example.
- `sent`: list of sentence IDs for each sentence within the example.
- `para`: list of paragraph IDs for each paragraph within the example.
- `word`: list of words.
- `terms`: layer of the terms resulting from the analysis of the original text (lemmatization, morphological,
PoS tagging)
- `tid`: list of term IDs for each term within the example.
- `lemma`: list of lemmas.
- `morphofeat`: list of morphological features.
- `pos`: list of PoS tags.
- `target`: list of sublists of the corresponding word IDs (normally, the sublists contain only one element,
in a one-to-one correspondence between words and terms).
- `opinions`: layer of the opinions in the text.
- `oid`: list of opinion IDs
- `opinion_holder_target`: list of sublists of the corresponding term IDs that span the opinion holder.
- `opinion_target_target`: list of sublists of the corresponding term IDs that span the opinion target.
- `opinion_expression_polarity`: list of the opinion expression polarities. The polarity can take one of the values:
`StrongNegative`, `Negative`, `Positive`, or `StrongPositive`.
- `opinion_expression_target`: list of sublists of the corresponding term IDs that span the opinion expression.

### Data Splits

[More Information Needed]

## 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

Dataset is under the [CC-BY 3.0](https://creativecommons.org/licenses/by/3.0/) license.

### Citation Information

```
@inproceedings{Barnes2018multibooked,
author={Barnes, Jeremy and Lambert, Patrik and Badia, Toni},
title={MultiBooked: A corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification},
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC'18)},
year = {2018},
month = {May},
date = {7-12},
address = {Miyazaki, Japan},
publisher = {European Language Resources Association (ELRA)},
language = {english}
}
```

### Contributions

Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

原始信息汇总

数据集概述

数据集名称: MultiBooked

语言:

• ca: 加泰罗尼亚语
• eu: 巴斯克语

许可证: CC-BY 3.0

多语言性: 单语种

大小分类: n<1K

源数据集: 原始数据

任务类别: 文本分类

任务ID: 情感分类

数据集结构

数据字段

• text
  • wid: 字符串，单词ID列表
  • sent: 字符串，句子ID列表
  • para: 字符串，段落ID列表
  • word: 字符串，单词列表
• terms
  • tid: 字符串，术语ID列表
  • lemma: 字符串，词干列表
  • morphofeat: 字符串，形态特征列表
  • pos: 字符串，词性标签列表
  • target: 字符串，目标序列
• opinions
  • oid: 字符串，意见ID列表
  • opinion_holder_target: 字符串，意见持有者目标序列
  • opinion_target_target: 字符串，意见目标目标序列
  • opinion_expression_polarity: 分类标签，极性值包括：StrongNegative, Negative, Positive, StrongPositive
  • opinion_expression_target: 字符串，意见表达目标序列

数据分割

• ca配置
  • train: 567个样本，1952731字节
• eu配置
  • train: 343个样本，1175816字节