Datasets:
Tasks:
Token Classification
Modalities:
Text
Languages:
English
Size:
100K - 1M
ArXiv:
Tags:
abbreviation-detection
License:
| 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](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/> | |
| # 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:** XX | |
| - **Leaderboard:** YY | |
| - **Point of Contact:** [Diptesh Kanojia](mailto:[email protected]) | |
| ### 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. | |
| ### Model(s) | |
| The working model is present [here at this link](https://huggingface.co/surrey-nlp/en_abbreviation_detection_roberta_lar).<br/> | |
| 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.<br/> | |
| #### Usage (in Python) | |
| You can use the HuggingFace Model link above to find the instructions for using this model in Python locally. | |