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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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language: |
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- en |
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license: |
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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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|
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# Dataset Card for STAN Large |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Dataset Creation](#dataset-creation) |
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- [Additional Information](#additional-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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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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Although `segmentation` has exactly the same characters as `hashtag` except for the spaces, the segmentations inside `alternatives` may have characters corrected to uppercase. |
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## Dataset Creation |
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- All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields: `hashtag` and `segmentation` or `identifier` and `segmentation`. |
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- The only difference between `hashtag` and `segmentation` or between `identifier` and `segmentation` are the whitespace characters. Spell checking, expanding abbreviations or correcting characters to uppercase go into other fields. |
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- There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as `_` , `:`, `~` ). |
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- If there are any annotations for named entity recognition and other token classification tasks, they are given in a `spans` field. |
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## Additional Information |
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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. |