|
--- |
|
annotations_creators: |
|
- crowdsourced |
|
language_creators: |
|
- found |
|
language: |
|
- en |
|
license: |
|
- unknown |
|
multilinguality: |
|
- monolingual |
|
size_categories: |
|
- 100K<n<1M |
|
- 10K<n<100K |
|
source_datasets: |
|
- original |
|
task_categories: |
|
- text-classification |
|
task_ids: |
|
- text-scoring |
|
- sentiment-classification |
|
- sentiment-scoring |
|
paperswithcode_id: sst |
|
pretty_name: Stanford Sentiment Treebank |
|
dataset_info: |
|
- config_name: default |
|
features: |
|
- name: sentence |
|
dtype: string |
|
- name: label |
|
dtype: float32 |
|
- name: tokens |
|
dtype: string |
|
- name: tree |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 2818768 |
|
num_examples: 8544 |
|
- name: validation |
|
num_bytes: 366205 |
|
num_examples: 1101 |
|
- name: test |
|
num_bytes: 730154 |
|
num_examples: 2210 |
|
download_size: 7162356 |
|
dataset_size: 3915127 |
|
- config_name: dictionary |
|
features: |
|
- name: phrase |
|
dtype: string |
|
- name: label |
|
dtype: float32 |
|
splits: |
|
- name: dictionary |
|
num_bytes: 12121843 |
|
num_examples: 239232 |
|
download_size: 7162356 |
|
dataset_size: 12121843 |
|
- config_name: ptb |
|
features: |
|
- name: ptb_tree |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 2185694 |
|
num_examples: 8544 |
|
- name: validation |
|
num_bytes: 284132 |
|
num_examples: 1101 |
|
- name: test |
|
num_bytes: 566248 |
|
num_examples: 2210 |
|
download_size: 7162356 |
|
dataset_size: 3036074 |
|
config_names: |
|
- default |
|
- dictionary |
|
- ptb |
|
--- |
|
|
|
# Dataset Card for sst |
|
|
|
## Table of Contents |
|
- [Dataset Description](#dataset-description) |
|
- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
|
- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
|
- [Curation Rationale](#curation-rationale) |
|
- [Source Data](#source-data) |
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- [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) |
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- [Additional Information](#additional-information) |
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- [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] |
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- **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 |
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- `tokens`: a sequence of tokens that form a sentence |
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- `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. |
