Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
machine-generated
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
word-segmentation
License:
annotations_creators: | |
- expert-generated | |
language_creators: | |
- machine-generated | |
language: | |
- en | |
license: | |
- agpl-3.0 | |
multilinguality: | |
- monolingual | |
size_categories: | |
- unknown | |
source_datasets: | |
- original | |
task_categories: | |
- structure-prediction | |
task_ids: [] | |
pretty_name: STAN Large | |
tags: | |
- 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. |