Datasets:
Commit
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Parent(s):
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Fix `license` metadata (#1)
Browse files- Fix `license` metadata (e3454351ea8da55eda4b8bed6639e560d3824b5b)
Co-authored-by: Julien Chaumond <[email protected]>
README.md
CHANGED
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@@ -1,148 +1,148 @@
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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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- hi
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- en
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-
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- unknown
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multilinguality:
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- multilingual
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pretty_name: HashSet Manual
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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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- named-entity-recognition
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- structure-prediction-other-word-segmentation
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---
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-
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# Dataset Card for HashSet Manual
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-
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## Dataset Description
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-
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- **Repository:** [prashantkodali/HashSet](https://github.com/prashantkodali/HashSet)
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- **Paper:** [HashSet -- A Dataset For Hashtag Segmentation](https://arxiv.org/abs/2201.06741)
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-
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### Dataset Summary
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Hashset is a new dataset consisting on 1.9k manually annotated and 3.3M loosely supervised tweets for testing the
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efficiency of hashtag segmentation models. We compare State of The Art Hashtag Segmentation models on Hashset and other
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baseline datasets (STAN and BOUN). We compare and analyse the results across the datasets to argue that HashSet can act
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as a good benchmark for hashtag segmentation tasks.
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-
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HashSet Manual: contains 1.9k manually annotated hashtags. Each row consists of the hashtag, segmented hashtag ,named entity annotations, whether the hashtag contains mix of hindi and english tokens and/or contains non-english tokens.
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-
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### Languages
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Mostly Hindi and 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": 10,
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"hashtag": "goodnewsmegan",
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"segmentation": "good news megan",
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"spans": {
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"start": [
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8
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],
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"end": [
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13
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],
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"text": [
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"megan"
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]
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},
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"source": "roman",
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"gold_position": null,
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"mix": false,
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"other": false,
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"ner": true,
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"annotator_id": 1,
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"annotation_id": 2088,
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"created_at": "2021-12-30 17:10:33.800607",
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"updated_at": "2021-12-30 17:10:59.714840",
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"lead_time": 3896.182,
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"rank": {
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"position": [
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1,
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2,
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3,
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4,
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5,
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6,
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7,
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8,
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9,
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10
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],
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"candidate": [
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"goodnewsmegan",
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"goodnewsmeg an",
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"goodnews megan",
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"goodnewsmega n",
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"go odnewsmegan",
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"good news megan",
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"good newsmegan",
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"g oodnewsmegan",
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"goodnewsme gan",
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"goodnewsm egan"
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]
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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 annotated by Kodali et al..
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- `hashtag`: the original hashtag.
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- `segmentation`: the gold segmentation for the hashtag.
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-
- `spans`: named entity spans.
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- `source`: data source.
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- `gold_position`: position of the gold segmentation on the `segmentation` field inside the `rank`.
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-
- `mix`: The hashtag has a mix of English and Hindi tokens.
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-
- `other`: The hashtag has non-English tokens.
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-
- `ner`: The hashtag has named entities.
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-
- `annotator_id`: annotator ID.
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- `annotation_id`: annotation ID.
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- `created_at`: Creation date timestamp.
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- `updated_at`: Update date timestamp.
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- `lead_time`: Lead time field annotated by Kodali et al..
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- `rank`: Rank of each candidate selected by a baseline word segmenter ( WordBreaker ).
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- `candidates`: Candidates selected by a baseline word segmenter ( WordBreaker ).
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-
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## Dataset Creation
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-
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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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-
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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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-
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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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-
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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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-
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## Additional Information
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-
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### Citation Information
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-
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```
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@article{kodali2022hashset,
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title={HashSet--A Dataset For Hashtag Segmentation},
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author={Kodali, Prashant and Bhatnagar, Akshala and Ahuja, Naman and Shrivastava, Manish and Kumaraguru, Ponnurangam},
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journal={arXiv preprint arXiv:2201.06741},
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year={2022}
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}
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```
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-
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### Contributions
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-
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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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| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- machine-generated
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| 6 |
+
language:
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| 7 |
+
- hi
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| 8 |
+
- en
|
| 9 |
+
license:
|
| 10 |
+
- unknown
|
| 11 |
+
multilinguality:
|
| 12 |
+
- multilingual
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| 13 |
+
pretty_name: HashSet Manual
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| 14 |
+
size_categories:
|
| 15 |
+
- unknown
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| 16 |
+
source_datasets:
|
| 17 |
+
- original
|
| 18 |
+
task_categories:
|
| 19 |
+
- structure-prediction
|
| 20 |
+
task_ids:
|
| 21 |
+
- named-entity-recognition
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| 22 |
+
- structure-prediction-other-word-segmentation
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| 23 |
+
---
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| 24 |
+
|
| 25 |
+
# Dataset Card for HashSet Manual
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| 26 |
+
|
| 27 |
+
## Dataset Description
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| 28 |
+
|
| 29 |
+
- **Repository:** [prashantkodali/HashSet](https://github.com/prashantkodali/HashSet)
|
| 30 |
+
- **Paper:** [HashSet -- A Dataset For Hashtag Segmentation](https://arxiv.org/abs/2201.06741)
|
| 31 |
+
|
| 32 |
+
### Dataset Summary
|
| 33 |
+
|
| 34 |
+
Hashset is a new dataset consisting on 1.9k manually annotated and 3.3M loosely supervised tweets for testing the
|
| 35 |
+
efficiency of hashtag segmentation models. We compare State of The Art Hashtag Segmentation models on Hashset and other
|
| 36 |
+
baseline datasets (STAN and BOUN). We compare and analyse the results across the datasets to argue that HashSet can act
|
| 37 |
+
as a good benchmark for hashtag segmentation tasks.
|
| 38 |
+
|
| 39 |
+
HashSet Manual: contains 1.9k manually annotated hashtags. Each row consists of the hashtag, segmented hashtag ,named entity annotations, whether the hashtag contains mix of hindi and english tokens and/or contains non-english tokens.
|
| 40 |
+
|
| 41 |
+
### Languages
|
| 42 |
+
|
| 43 |
+
Mostly Hindi and English.
|
| 44 |
+
|
| 45 |
+
## Dataset Structure
|
| 46 |
+
|
| 47 |
+
### Data Instances
|
| 48 |
+
|
| 49 |
+
```
|
| 50 |
+
{
|
| 51 |
+
"index": 10,
|
| 52 |
+
"hashtag": "goodnewsmegan",
|
| 53 |
+
"segmentation": "good news megan",
|
| 54 |
+
"spans": {
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| 55 |
+
"start": [
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| 56 |
+
8
|
| 57 |
+
],
|
| 58 |
+
"end": [
|
| 59 |
+
13
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| 60 |
+
],
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| 61 |
+
"text": [
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+
"megan"
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+
]
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+
},
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+
"source": "roman",
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+
"gold_position": null,
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+
"mix": false,
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+
"other": false,
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+
"ner": true,
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+
"annotator_id": 1,
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+
"annotation_id": 2088,
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+
"created_at": "2021-12-30 17:10:33.800607",
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+
"updated_at": "2021-12-30 17:10:59.714840",
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+
"lead_time": 3896.182,
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+
"rank": {
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+
"position": [
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+
1,
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+
2,
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+
3,
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+
4,
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+
5,
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+
6,
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7,
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8,
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+
9,
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+
10
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],
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"candidate": [
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"goodnewsmegan",
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+
"goodnewsmeg an",
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| 91 |
+
"goodnews megan",
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+
"goodnewsmega n",
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+
"go odnewsmegan",
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+
"good news megan",
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| 95 |
+
"good newsmegan",
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+
"g oodnewsmegan",
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| 97 |
+
"goodnewsme gan",
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+
"goodnewsm egan"
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+
]
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+
}
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+
}
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+
```
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| 103 |
+
|
| 104 |
+
### Data Fields
|
| 105 |
+
|
| 106 |
+
- `index`: a numerical index annotated by Kodali et al..
|
| 107 |
+
- `hashtag`: the original hashtag.
|
| 108 |
+
- `segmentation`: the gold segmentation for the hashtag.
|
| 109 |
+
- `spans`: named entity spans.
|
| 110 |
+
- `source`: data source.
|
| 111 |
+
- `gold_position`: position of the gold segmentation on the `segmentation` field inside the `rank`.
|
| 112 |
+
- `mix`: The hashtag has a mix of English and Hindi tokens.
|
| 113 |
+
- `other`: The hashtag has non-English tokens.
|
| 114 |
+
- `ner`: The hashtag has named entities.
|
| 115 |
+
- `annotator_id`: annotator ID.
|
| 116 |
+
- `annotation_id`: annotation ID.
|
| 117 |
+
- `created_at`: Creation date timestamp.
|
| 118 |
+
- `updated_at`: Update date timestamp.
|
| 119 |
+
- `lead_time`: Lead time field annotated by Kodali et al..
|
| 120 |
+
- `rank`: Rank of each candidate selected by a baseline word segmenter ( WordBreaker ).
|
| 121 |
+
- `candidates`: Candidates selected by a baseline word segmenter ( WordBreaker ).
|
| 122 |
+
|
| 123 |
+
## Dataset Creation
|
| 124 |
+
|
| 125 |
+
- All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields: `hashtag` and `segmentation` or `identifier` and `segmentation`.
|
| 126 |
+
|
| 127 |
+
- 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.
|
| 128 |
+
|
| 129 |
+
- There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as `_` , `:`, `~` ).
|
| 130 |
+
|
| 131 |
+
- If there are any annotations for named entity recognition and other token classification tasks, they are given in a `spans` field.
|
| 132 |
+
|
| 133 |
+
## Additional Information
|
| 134 |
+
|
| 135 |
+
### Citation Information
|
| 136 |
+
|
| 137 |
+
```
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| 138 |
+
@article{kodali2022hashset,
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| 139 |
+
title={HashSet--A Dataset For Hashtag Segmentation},
|
| 140 |
+
author={Kodali, Prashant and Bhatnagar, Akshala and Ahuja, Naman and Shrivastava, Manish and Kumaraguru, Ponnurangam},
|
| 141 |
+
journal={arXiv preprint arXiv:2201.06741},
|
| 142 |
+
year={2022}
|
| 143 |
+
}
|
| 144 |
+
```
|
| 145 |
+
|
| 146 |
+
### Contributions
|
| 147 |
+
|
| 148 |
This dataset was added by [@ruanchaves](https://github.com/ruanchaves) while developing the [hashformers](https://github.com/ruanchaves/hashformers) library.
|