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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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- agpl-3.0
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multilinguality:
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- monolingual
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pretty_name: STAN Large
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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 Large
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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": 15,
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"hashtag": "PokemonPlatinum",
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"segmentation": "Pokemon platinum",
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"alternatives": {
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"segmentation": [
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"Pokemon Platinum"
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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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@inproceedings{maddela-etal-2019-multi,
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title = "Multi-task Pairwise Neural Ranking for Hashtag Segmentation",
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author = "Maddela, Mounica and
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Xu, Wei and
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Preo{\c{t}}iuc-Pietro, Daniel",
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booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
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month = jul,
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year = "2019",
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address = "Florence, Italy",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/P19-1242",
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doi = "10.18653/v1/P19-1242",
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pages = "2538--2549",
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abstract = "Hashtags are often employed on social media and beyond to add metadata to a textual utterance with the goal of increasing discoverability, aiding search, or providing additional semantics. However, the semantic content of hashtags is not straightforward to infer as these represent ad-hoc conventions which frequently include multiple words joined together and can include abbreviations and unorthodox spellings. We build a dataset of 12,594 hashtags split into individual segments and propose a set of approaches for hashtag segmentation by framing it as a pairwise ranking problem between candidate segmentations. Our novel neural approaches demonstrate 24.6{\%} error reduction in hashtag segmentation accuracy compared to the current state-of-the-art method. Finally, we demonstrate that a deeper understanding of hashtag semantics obtained through segmentation is useful for downstream applications such as sentiment analysis, for which we achieved a 2.6{\%} increase in average recall on the SemEval 2017 sentiment analysis dataset.",
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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. |