Fix `license` metadata (#1)

- Fix `license` metadata (cb105f710c98c1b6494015fbdfa11fc14514ffa7)


Co-authored-by: Julien Chaumond <[email protected]>

README.md

@@ -1,122 +1,122 @@
----
-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
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-- [Dataset Creation](#dataset-creation)
-- [Additional Information](#additional-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## 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.
-
-Although `segmentation` has exactly the same characters as `hashtag` except for the spaces, the segmentations inside `alternatives` may have characters corrected to uppercase.
-
-## Dataset Creation
-
-- All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields: `hashtag` and `segmentation` or `identifier` and `segmentation`.
-
-- 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.
-
-- There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as `_` , `:`, `~` ). 
-
-- If there are any annotations for named entity recognition and other token classification tasks, they are given in a `spans` field.
-
-## Additional Information
-
-### 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
-
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- machine-generated
+language:
+- en
+license:
+- 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
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+- [Dataset Creation](#dataset-creation)
+- [Additional Information](#additional-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## 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.
+
+Although `segmentation` has exactly the same characters as `hashtag` except for the spaces, the segmentations inside `alternatives` may have characters corrected to uppercase.
+
+## Dataset Creation
+
+- All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields: `hashtag` and `segmentation` or `identifier` and `segmentation`.
+
+- 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.
+
+- There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as `_` , `:`, `~` ). 
+
+- If there are any annotations for named entity recognition and other token classification tasks, they are given in a `spans` field.
+
+## Additional Information
+
+### 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.