Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
machine-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
word-segmentation
License:
ruanchaves
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README.md
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---
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-
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---
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---
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annotations_creators:
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- expert-generated
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language_creators:
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- machine-generated
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languages:
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- en
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licenses:
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- unknown
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multilinguality:
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- monolingual
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pretty_name: STAN Small
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size_categories:
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- unknown
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source_datasets:
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- original
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task_categories:
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- structure-prediction
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task_ids:
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- structure-prediction-other-word-segmentation
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---
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# Dataset Card for STAN Small
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## Dataset Description
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- **Repository:** [mounicam/hashtag_master](https://github.com/mounicam/hashtag_master)
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- **Paper:** [Multi-task Pairwise Neural Ranking for Hashtag Segmentation](https://aclanthology.org/P19-1242/)
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### Dataset Summary
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The description below was taken from the paper "Multi-task Pairwise Neural Ranking for Hashtag Segmentation"
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by Maddela et al..
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"STAN large, our new expert curated dataset, which includes all 12,594 unique English hashtags and their
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associated tweets from the same Stanford dataset.
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STAN small is the most commonly used dataset in previous work. However, after reexamination, we found annotation
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errors in 6.8% of the hashtags in this dataset, which is significant given that the error rate of the state-of-the art
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models is only around 10%. Most of the errors were related to named entities. For example, #lionhead,
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which refers to the “Lionhead” video game company, was labeled as “lion head”.
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We therefore constructed the STAN large dataset of 12,594 hashtags with additional quality control for human annotations."
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### Languages
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English
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## Dataset Structure
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### Data Instances
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```
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{
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"index": 6,
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"hashtag": "justsayin",
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"segmentation": "just sayin",
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"alternatives": {
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"segmentation": [
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"just sayin",
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"just sayin "
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]
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}
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}
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```
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### Data Fields
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- `index`: a numerical index.
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- `hashtag`: the original hashtag.
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- `segmentation`: the gold segmentation for the hashtag.
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- `alternatives`: other segmentations that are also accepted as a gold segmentation.
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### Citation Information
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```
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@misc{bansal2015deep,
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title={Towards Deep Semantic Analysis Of Hashtags},
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author={Piyush Bansal and Romil Bansal and Vasudeva Varma},
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year={2015},
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eprint={1501.03210},
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archivePrefix={arXiv},
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primaryClass={cs.IR}
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}
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```
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### Contributions
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This dataset was added by [@ruanchaves](https://github.com/ruanchaves) while developing the [hashformers](https://github..com/ruanchaves/hashformers) library.
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