surrey-nlp/PLOD-unfiltered

---
annotations_creators:
- Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids: []
paperswithcode_id: plod-an-abbreviation-detection-dataset-for
pretty_name: 'PLOD: An Abbreviation Detection Dataset'
tags:
- abbreviation-detection
---

# PLOD: An Abbreviation Detection Dataset

This is the repository for PLOD Dataset published at LREC 2022. The dataset can help build sequence labelling models for the task Abbreviation Detection.

### Dataset

We provide two variants of our dataset - Filtered and Unfiltered. They are described in our paper here.

1. The Filtered version can be accessed via [Huggingface Datasets here](https://huggingface.co/datasets/surrey-nlp/PLOD-filtered) and a [CONLL format is present here](https://github.com/surrey-nlp/PLOD-AbbreviationDetection).<br/>

2. The Unfiltered version can be accessed via [Huggingface Datasets here](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) and a [CONLL format is present here](https://github.com/surrey-nlp/PLOD-AbbreviationDetection).<br/>

3. The [SDU Shared Task](https://sites.google.com/view/sdu-aaai22/home) data we use for zero-shot testing is [available here](https://huggingface.co/datasets/surrey-nlp/SDU-test).

# Dataset Card for PLOD-unfiltered

## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)

## Dataset Description

- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/surrey-nlp/PLOD-AbbreviationDetection
- **Paper:** https://arxiv.org/abs/2204.12061
- **Leaderboard:** https://paperswithcode.com/sota/abbreviationdetection-on-plod-an-abbreviation
- **Point of Contact:** [Diptesh Kanojia](mailto:[e-mail])

### Dataset Summary

This PLOD Dataset is an English-language dataset of abbreviations and their long-forms tagged in text. The dataset has been collected for research from the PLOS journals indexing of abbreviations and long-forms in the text. This dataset was created to support the Natural Language Processing task of abbreviation detection and covers the scientific domain.

### Supported Tasks and Leaderboards

This dataset primarily supports the Abbreviation Detection Task. It has also been tested on a train+dev split provided by the Acronym Detection Shared Task organized as a part of the Scientific Document Understanding (SDU) workshop at AAAI 2022.

### Languages

English

## Dataset Structure

### Data Instances

A typical data point comprises an ID, a set of `tokens` present in the text, a set of `pos_tags` for the corresponding tokens obtained via Spacy NER, and a set of `ner_tags` which are limited to `AC` for `Acronym` and `LF` for `long-forms`.

An example from the dataset:
{'id': '1',
'tokens': ['Study', '-', 'specific', 'risk', 'ratios', '(', 'RRs', ')', 'and', 'mean', 'BW', 'differences', 'were', 'calculated', 'using', 'linear', 'and', 'log', '-', 'binomial', 'regression', 'models', 'controlling', 'for', 'confounding', 'using', 'inverse', 'probability', 'of', 'treatment', 'weights', '(', 'IPTW', ')', 'truncated', 'at', 'the', '1st', 'and', '99th', 'percentiles', '.'],
'pos_tags': [8, 13, 0, 8, 8, 13, 12, 13, 5, 0, 12, 8, 3, 16, 16, 0, 5, 0, 13, 0, 8, 8, 16, 1, 8, 16, 0, 8, 1, 8, 8, 13, 12, 13, 16, 1, 6, 0, 5, 0, 8, 13],
'ner_tags': [0, 0, 0, 3, 4, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}

### Data Fields

- id: the row identifier for the dataset point.
- tokens: The tokens contained in the text.
- pos_tags: the Part-of-Speech tags obtained for the corresponding token above from Spacy NER.
- ner_tags: The tags for abbreviations and long-forms.

### Data Splits

| | Train | Valid | Test |
| ----- | ------ | ----- | ---- |
| Filtered | 112652 | 24140 | 24140|
| Unfiltered | 113860 | 24399 | 24399|

## Dataset Creation

### Source Data

#### Initial Data Collection and Normalization

Extracting the data from PLOS Journals online and then tokenization, normalization.

#### Who are the source language producers?

PLOS Journal

## Additional Information

### Dataset Curators

The dataset was initially created by Leonardo Zilio, Hadeel Saadany, Prashant Sharma,
Diptesh Kanojia, Constantin Orasan.

### Licensing Information

CC-BY-SA 4.0

### Citation Information

[Needs More Information]

### Installation

We use the custom NER pipeline in the [spaCy transformers](https://spacy.io/universe/project/spacy-transformers) library to train our models. This library supports training via any pre-trained language models available at the :rocket: [HuggingFace repository](https://huggingface.co/).<br/>
Please see the instructions at these websites to setup your own custom training with our dataset to reproduce the experiments using Spacy.

OR<br/>

However, you can also reproduce the experiments via the Python notebook we [provide here](https://github.com/surrey-nlp/PLOD-AbbreviationDetection/blob/main/nbs/fine_tuning_abbr_det.ipynb) which uses HuggingFace Trainer class to perform the same experiments. The exact hyperparameters can be obtained from the models readme cards linked below. Before starting, please perform the following steps:

```bash
git clone https://github.com/surrey-nlp/PLOD-AbbreviationDetection
cd PLOD-AbbreviationDetection
pip install -r requirements.txt
```

Now, you can use the notebook to reproduce the experiments.

### Model(s)

Our best performing models are hosted on the HuggingFace models repository:

| Models | [`PLOD - Unfiltered`](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) | [`PLOD - Filtered`](https://huggingface.co/datasets/surrey-nlp/PLOD-filtered) | Description |
| --- | :---: | :---: | --- |
| [RoBERTa_large](https://huggingface.co/roberta-large) | [RoBERTa_large-finetuned-abbr](https://huggingface.co/surrey-nlp/roberta-large-finetuned-abbr) | -soon- | Fine-tuning on the RoBERTa_large language model |
| [RoBERTa_base](https://huggingface.co/roberta-base) | -soon- | [RoBERTa_base-finetuned-abbr](https://huggingface.co/surrey-nlp/roberta-large-finetuned-abbr) | Fine-tuning on the RoBERTa_base language model |
| [AlBERT_large-v2](https://huggingface.co/albert-large-v2) | [AlBERT_large-v2-finetuned-abbDet](https://huggingface.co/surrey-nlp/albert-large-v2-finetuned-abbDet) | -soon- | Fine-tuning on the AlBERT_large-v2 language model |

On the link provided above, the model(s) can be used with the help of the Inference API via the web-browser itself. We have placed some examples with the API for testing.<br/>

### Usage

You can use the HuggingFace Model link above to find the instructions for using this model in Python locally using the notebook provided in the Git repo.

原始信息汇总

数据集概述

名称: PLOD: An Abbreviation Detection Dataset
创建者: Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan
语言: 英语
许可证: CC-BY-SA 4.0
多语言性: 单语
大小: 100K<n<1M
来源: 原始数据
任务类别: 词元分类
标签: 缩写检测

数据集详情

数据集描述

• 摘要: PLOD数据集是一个英语语言数据集，包含文本中的缩写及其长形式。该数据集从PLOS期刊中收集，用于支持自然语言处理任务中的缩写检测，主要覆盖科学领域。
• 支持任务: 主要支持缩写检测任务，并已在AAAI 2022的科学文档理解研讨会的Acronym Detection Shared Task中进行了测试。

数据集结构

• 数据实例: 每个数据点包含ID、文本中的词元集合、对应的词性标签和缩写及长形式的标签。
• 数据字段:
  • id: 数据点的行标识。
  • tokens: 文本中包含的词元。
  • pos_tags: 对应的词性标签。
  • ner_tags: 缩写和长形式的标签。
• 数据分割:
  • Filtered: 训练112652条，验证24140条，测试24140条。
  • Unfiltered: 训练113860条，验证24399条，测试24399条。

数据集创建

• 源数据: 从PLOS期刊在线提取数据，进行分词和规范化处理。
• 语言生产者: PLOS期刊。

附加信息

• 数据集维护者: Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan。
• 许可证信息: CC-BY-SA 4.0。
• 引用信息: 待补充。

数据集使用

• 模型: 最佳表现模型托管在HuggingFace模型库中，包括RoBERTa和AlBERT的微调版本。
• 使用指南: 可通过提供的Python笔记本或使用HuggingFace Trainer类进行模型训练和实验复现。