README.md

annotations_creators:

• Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan language_creators:
• found languages:
• en licenses:
• cc-by-sa4.0 multilinguality:
• monolingual paperswithcode_id: acronym-identification pretty_name: 'PLOD: An Abbreviation Detection Dataset' size_categories:
• 100K<n<1M source_datasets:
• original task_categories:
• token-classification task_ids:
• named-entity-recognition

PLOD: An Abbreviation Detection Dataset

This is the repository for PLOD Dataset submitted to 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 and a CONLL format is present here.
   

2. The Unfiltered version can be accessed via Huggingface Datasets here and a CONLL format is present here.
   

Dataset Card for PLOD-unfiltered

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information

Dataset Description

• Homepage: [Needs More Information]
• Repository: https://github.com/surrey-nlp/PLOD-AbbreviationDetection
• Paper: XX
• Leaderboard: YY
• Point of Contact: Diptesh Kanojia

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 library to train our models. This library supports training via any pre-trained language models available at the :rocket: HuggingFace repository.
Please see the instructions at these websites to setup your own custom training with our dataset.

Model(s)

The working model is present here at this link.
On the link provided above, the model 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.

Usage (in Python)

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