Update README.md

README.md

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 ---
-license: agpl-3.0
+annotations_creators:
+- expert-generated
+language_creators:
+- machine-generated
+languages:
+- en
+licenses:
+- agpl-3.0
+multilinguality:
+- monolingual
+pretty_name: STAN Large
+size_categories:
+- unknown
+source_datasets:
+- original
+task_categories:
+- structure-prediction
+task_ids:
+- structure-prediction-other-word-segmentation
 ---
+
+# Dataset Card for STAN Large
+
+## Dataset Description
+
+- **Repository:** [mounicam/hashtag_master](https://github.com/mounicam/hashtag_master)
+- **Paper:** [Multi-task Pairwise Neural Ranking for Hashtag Segmentation](https://aclanthology.org/P19-1242/)
+
+### Dataset Summary
+
+The description below was taken from the paper "Multi-task Pairwise Neural Ranking for Hashtag Segmentation"
+by Maddela et al..
+
+"STAN large, our new expert curated dataset, which includes all 12,594 unique English hashtags and their 
+associated tweets from the same Stanford dataset.
+
+STAN small is the most commonly used dataset in previous work. However, after reexamination, we found annotation 
+errors in 6.8% of the hashtags in this dataset, which is significant given that the error rate of the state-of-the art 
+models is only around 10%. Most of the errors were related to named entities. For example, #lionhead, 
+which refers to the “Lionhead” video game company, was labeled as “lion head”.
+
+We therefore constructed the STAN large dataset of 12,594 hashtags with additional quality control for human annotations."
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+```
+{
+    "index": 15,
+    "hashtag": "PokemonPlatinum",
+    "segmentation": "Pokemon platinum",
+    "alternatives": {
+        "segmentation": [
+            "Pokemon Platinum"
+        ]
+    }
+}
+```
+
+### Data Fields
+
+- `index`: a numerical index.
+- `hashtag`: the original hashtag.
+- `segmentation`: the gold segmentation for the hashtag.
+- `alternatives`: other segmentations that are also accepted as a gold segmentation.
+
+### Citation Information
+
+```
+@inproceedings{maddela-etal-2019-multi,
+    title = "Multi-task Pairwise Neural Ranking for Hashtag Segmentation",
+    author = "Maddela, Mounica  and
+      Xu, Wei  and
+      Preo{\c{t}}iuc-Pietro, Daniel",
+    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
+    month = jul,
+    year = "2019",
+    address = "Florence, Italy",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/P19-1242",
+    doi = "10.18653/v1/P19-1242",
+    pages = "2538--2549",
+    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.",
+}
+```
+
+### Contributions
+
+This dataset was added by [@ruanchaves](https://github.com/ruanchaves) while developing the [hashformers](https://github..com/ruanchaves/hashformers) library.