sst

# Dataset Card for sst

## 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:** https://nlp.stanford.edu/sentiment/index.html
- **Repository:** [Needs More Information]
- **Paper:** [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank](https://www.aclweb.org/anthology/D13-1170/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]

### Dataset Summary

The Stanford Sentiment Treebank is the first corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language.

### Supported Tasks and Leaderboards

- `sentiment-scoring`: Each complete sentence is annotated with a `float` label that indicates its level of positive sentiment from 0.0 to 1.0. One can decide to use only complete sentences or to include the contributions of the sub-sentences (aka phrases). The labels for each phrase are included in the `dictionary` configuration. To obtain all the phrases in a sentence we need to visit the parse tree included with each example. In contrast, the `ptb` configuration explicitly provides all the labelled parse trees in Penn Treebank format. Here the labels are binned in 5 bins from 0 to 4.
- `sentiment-classification`: We can transform the above into a binary sentiment classification task by rounding each label to 0 or 1.

### Languages

The text in the dataset is in English

## Dataset Structure

### Data Instances

For the `default` configuration:
```
{'label': 0.7222200036048889,
'sentence': 'Yet the act is still charming here .',
'tokens': 'Yet|the|act|is|still|charming|here|.',
'tree': '15|13|13|10|9|9|11|12|10|11|12|14|14|15|0'}
```

For the `dictionary` configuration:
```
{'label': 0.7361099720001221,
'phrase': 'still charming'}
```

For the `ptb` configuration:
```
{'ptb_tree': '(3 (2 Yet) (3 (2 (2 the) (2 act)) (3 (4 (3 (2 is) (3 (2 still) (4 charming))) (2 here)) (2 .))))'}
```

### Data Fields

- `sentence`: a complete sentence expressing an opinion about a film
- `label`: the degree of "positivity" of the opinion, on a scale between 0.0 and 1.0
- `tokens`: a sequence of tokens that form a sentence
- `tree`: a sentence parse tree formatted as a parent pointer tree
- `phrase`: a sub-sentence of a complete sentence
- `ptb_tree`: a sentence parse tree formatted in Penn Treebank-style, where each component's degree of positive sentiment is labelled on a scale from 0 to 4

### Data Splits

The set of complete sentences (both `default` and `ptb` configurations) is split into a training, validation and test set. The `dictionary` configuration has only one split as it is used for reference rather than for learning.

## Dataset Creation

### Curation Rationale

[Needs More Information]

### Source Data

#### Initial Data Collection and Normalization

[Needs More Information]

#### Who are the source language producers?

Rotten Tomatoes reviewers.

### Annotations

#### Annotation process

[Needs More Information]

#### Who are the annotators?

[Needs More Information]

### Personal and Sensitive Information

[Needs More Information]

## Considerations for Using the Data

### Social Impact of Dataset

[Needs More Information]

### Discussion of Biases

[Needs More Information]

### Other Known Limitations

[Needs More Information]

## Additional Information

### Dataset Curators

[Needs More Information]

### Licensing Information

[Needs More Information]

### Citation Information

```
@inproceedings{socher-etal-2013-recursive,
title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
author = "Socher, Richard and
Perelygin, Alex and
Wu, Jean and
Chuang, Jason and
Manning, Christopher D. and
Ng, Andrew and
Potts, Christopher",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D13-1170",
pages = "1631--1642",
}
```

### Contributions

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

# sst数据集卡片

## 目录
- [数据集描述](#dataset-description)
- [数据集概述](#dataset-summary)
- [支持任务与排行榜](#supported-tasks-and-leaderboards)
- [语言](#languages)
- [数据集结构](#dataset-structure)
- [数据实例](#data-instances)
- [数据字段](#data-fields)
- [数据划分](#data-splits)
- [数据集构建](#dataset-creation)
- [构建初衷](#curation-rationale)
- [源数据](#source-data)
- [标注信息](#annotations)
- [个人与敏感信息](#personal-and-sensitive-information)
- [数据集使用注意事项](#considerations-for-using-the-data)
- [数据集社会影响](#social-impact-of-dataset)
- [偏差讨论](#discussion-of-biases)
- [其他已知局限性](#other-known-limitations)
- [附加信息](#additional-information)
- [数据集维护者](#dataset-curators)
- [授权信息](#licensing-information)
- [引用信息](#citation-information)
- [贡献者](#contributions)

## 数据集描述

- **"主页"**：https://nlp.stanford.edu/sentiment/index.html
- **"代码仓库"**：[需补充更多信息]
- **"论文"**：《用于情感树库语义组合性的递归深度模型》(Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank)，链接：https://www.aclweb.org/anthology/D13-1170/
- **"排行榜"**：[需补充更多信息]
- **"联系方式"**：[需补充更多信息]

### 数据集概述
斯坦福情感树库（Stanford Sentiment Treebank）是首个带有全标注句法树的语料库，可完整分析语言中情感的组合效应。

### 支持任务与排行榜
- `情感评分（sentiment-scoring）`：每个完整句子会被标注一个浮点型（float）标签，取值范围为0.0至1.0，用于表征其正面情感强度。研究者可选择仅使用完整句子，或纳入子句（即短语）的情感贡献。每个短语的标签已包含在`dictionary`配置中。若要获取句子中的所有短语，需解析每个样本附带的句法树。与之相对，`ptb`配置会以Penn Treebank（Penn Treebank）格式显式提供所有带标注的句法树，此时标签被划分为0至4共5个区间。
- `情感分类（sentiment-classification）`：可将上述任务转化为二分类情感分类任务，即把每个标签四舍五入为0或1。

### 语言
本数据集的文本语言为英语。

## 数据集结构

### 数据实例
针对`default`配置：

{'label': 0.7222200036048889,
'sentence': 'Yet the act is still charming here .',
'tokens': 'Yet|the|act|is|still|charming|here|.',
'tree': '15|13|13|10|9|9|11|12|10|11|12|14|14|15|0'}

针对`dictionary`配置：

{'label': 0.7361099720001221,
'phrase': 'still charming'}

针对`ptb`配置：

{'ptb_tree': '(3 (2 Yet) (3 (2 (2 the) (2 act)) (3 (4 (3 (2 is) (3 (2 still) (4 charming))) (2 here)) (2 .))))'}

### 数据字段
- `sentence`：表达对某部电影观点的完整句子
- `label`：观点的正面程度，取值范围为0.0至1.0的连续区间
- `tokens`：构成句子的Token（Token）序列
- `tree`：以父指针树格式表示的句子句法树
- `phrase`：完整句子的子句片段
- `ptb_tree`：采用Penn Treebank格式的句子句法树，其中每个组成部分的正面情感强度以0至4的区间标注

### 数据划分
完整句子的数据集（包括`default`和`ptb`配置）被划分为训练集、验证集与测试集。而`dictionary`配置仅包含一个划分，因其主要用于参考而非模型训练。

## 数据集构建

### 构建初衷
[需补充更多信息]

### 源数据
#### 初始数据收集与标准化
[需补充更多信息]
#### 源文本生产者是谁？
烂番茄（Rotten Tomatoes）的影评人。

### 标注信息
#### 标注流程
[需补充更多信息]
#### 标注者是谁？
[需补充更多信息]

### 个人与敏感信息
[需补充更多信息]

## 数据集使用注意事项

### 数据集社会影响
[需补充更多信息]

### 偏差讨论
[需补充更多信息]

### 其他已知局限性
[需补充更多信息]

## 附加信息

### 数据集维护者
[需补充更多信息]

### 授权信息
[需补充更多信息]

### 引用信息

@inproceedings{socher-etal-2013-recursive,
title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
author = "Socher, Richard and
Perelygin, Alex and
Wu, Jean and
Chuang, Jason and
Manning, Christopher D. and
Ng, Andrew and
Potts, Christopher",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D13-1170",
pages = "1631--1642",
}

其中论文标题可译为《用于情感树库语义组合性的递归深度模型》，会议名称可译为《2013年自然语言处理经验方法会议论文集》，出版方可译为国际计算语言学协会（Association for Computational Linguistics）。

### 贡献者
感谢[@patpizio](https://github.com/patpizio)贡献本数据集。