Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
acronym-identification
License:
| annotations_creators: | |
| - expert-generated | |
| language_creators: | |
| - found | |
| language: | |
| - en | |
| license: | |
| - mit | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 10K<n<100K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - token-classification | |
| task_ids: [] | |
| paperswithcode_id: acronym-identification | |
| pretty_name: Acronym Identification Dataset | |
| tags: | |
| - acronym-identification | |
| dataset_info: | |
| features: | |
| - name: id | |
| dtype: string | |
| - name: tokens | |
| sequence: string | |
| - name: labels | |
| sequence: | |
| class_label: | |
| names: | |
| '0': B-long | |
| '1': B-short | |
| '2': I-long | |
| '3': I-short | |
| '4': O | |
| splits: | |
| - name: train | |
| num_bytes: 7792803 | |
| num_examples: 14006 | |
| - name: validation | |
| num_bytes: 952705 | |
| num_examples: 1717 | |
| - name: test | |
| num_bytes: 987728 | |
| num_examples: 1750 | |
| download_size: 8556464 | |
| dataset_size: 9733236 | |
| train-eval-index: | |
| - config: default | |
| task: token-classification | |
| task_id: entity_extraction | |
| splits: | |
| eval_split: test | |
| col_mapping: | |
| tokens: tokens | |
| labels: tags | |
| # Dataset Card for Acronym Identification Dataset | |
| ## Table of Contents | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [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) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Homepage:** https://sites.google.com/view/sdu-aaai21/shared-task | |
| - **Repository:** https://github.com/amirveyseh/AAAI-21-SDU-shared-task-1-AI | |
| - **Paper:** [What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation](https://arxiv.org/pdf/2010.14678v1.pdf) | |
| - **Leaderboard:** https://competitions.codalab.org/competitions/26609 | |
| - **Point of Contact:** [More Information Needed] | |
| ### Dataset Summary | |
| This dataset contains the training, validation, and test data for the **Shared Task 1: Acronym Identification** of the AAAI-21 Workshop on Scientific Document Understanding. | |
| ### Supported Tasks and Leaderboards | |
| The dataset supports an `acronym-identification` task, where the aim is to predic which tokens in a pre-tokenized sentence correspond to acronyms. The dataset was released for a Shared Task which supported a [leaderboard](https://competitions.codalab.org/competitions/26609). | |
| ### Languages | |
| The sentences in the dataset are in English (`en`). | |
| ## Dataset Structure | |
| ### Data Instances | |
| A sample from the training set is provided below: | |
| ``` | |
| {'id': 'TR-0', | |
| 'labels': [4, 4, 4, 4, 0, 2, 2, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4], | |
| 'tokens': ['What', | |
| 'is', | |
| 'here', | |
| 'called', | |
| 'controlled', | |
| 'natural', | |
| 'language', | |
| '(', | |
| 'CNL', | |
| ')', | |
| 'has', | |
| 'traditionally', | |
| 'been', | |
| 'given', | |
| 'many', | |
| 'different', | |
| 'names', | |
| '.']} | |
| ``` | |
| Please note that in test set sentences only the `id` and `tokens` fields are available. `labels` can be ignored for test set. Labels in the test set are all `O` | |
| ### Data Fields | |
| The data instances have the following fields: | |
| - `id`: a `string` variable representing the example id, unique across the full dataset | |
| - `tokens`: a list of `string` variables representing the word-tokenized sentence | |
| - `labels`: a list of `categorical` variables with possible values `["B-long", "B-short", "I-long", "I-short", "O"]` corresponding to a BIO scheme. `-long` corresponds to the expanded acronym, such as *controlled natural language* here, and `-short` to the abbrviation, `CNL` here. | |
| ### Data Splits | |
| The training, validation, and test set contain `14,006`, `1,717`, and `1750` sentences respectively. | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| > First, most of the existing datasets for acronym identification (AI) are either limited in their sizes or created using simple rule-based methods. | |
| > This is unfortunate as rules are in general not able to capture all the diverse forms to express acronyms and their long forms in text. | |
| > Second, most of the existing datasets are in the medical domain, ignoring the challenges in other scientific domains. | |
| > In order to address these limitations this paper introduces two new datasets for Acronym Identification. | |
| > Notably, our datasets are annotated by human to achieve high quality and have substantially larger numbers of examples than the existing AI datasets in the non-medical domain. | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| > In order to prepare a corpus for acronym annotation, we collect a corpus of 6,786 English papers from arXiv. | |
| > These papers consist of 2,031,592 sentences that would be used for data annotation for AI in this work. | |
| The dataset paper does not report the exact tokenization method. | |
| #### Who are the source language producers? | |
| The language was comes from papers hosted on the online digital archive [arXiv](https://arxiv.org/). No more information is available on the selection process or identity of the writers. | |
| ### Annotations | |
| #### Annotation process | |
| > Each sentence for annotation needs to contain at least one word in which more than half of the characters in are capital letters (i.e., acronym candidates). | |
| > Afterward, we search for a sub-sequence of words in which the concatenation of the first one, two or three characters of the words (in the order of the words in the sub-sequence could form an acronym candidate. | |
| > We call the sub-sequence a long form candidate. If we cannot find any long form candidate, we remove the sentence. | |
| > Using this process, we end up with 17,506 sentences to be annotated manually by the annotators from Amazon Mechanical Turk (MTurk). | |
| > In particular, we create a HIT for each sentence and ask the workers to annotate the short forms and the long forms in the sentence. | |
| > In case of disagreements, if two out of three workers agree on an annotation, we use majority voting to decide the correct annotation. | |
| > Otherwise, a fourth annotator is hired to resolve the conflict | |
| #### Who are the annotators? | |
| Workers were recruited through Amazon MEchanical Turk and paid $0.05 per annotation. No further demographic information is provided. | |
| ### Personal and Sensitive Information | |
| Papers published on arXiv are unlikely to contain much personal information, although some do include some poorly chosen examples revealing personal details, so the data should be used with care. | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [More Information Needed] | |
| ### Discussion of Biases | |
| [More Information Needed] | |
| ### Other Known Limitations | |
| Dataset provided for research purposes only. Please check dataset license for additional information. | |
| ## Additional Information | |
| ### Dataset Curators | |
| [More Information Needed] | |
| ### Licensing Information | |
| The dataset provided for this shared task is licensed under CC BY-NC-SA 4.0 international license. | |
| ### Citation Information | |
| ``` | |
| @inproceedings{Veyseh2020, | |
| author = {Amir Pouran Ben Veyseh and | |
| Franck Dernoncourt and | |
| Quan Hung Tran and | |
| Thien Huu Nguyen}, | |
| editor = {Donia Scott and | |
| N{\'{u}}ria Bel and | |
| Chengqing Zong}, | |
| title = {What Does This Acronym Mean? Introducing a New Dataset for Acronym | |
| Identification and Disambiguation}, | |
| booktitle = {Proceedings of the 28th International Conference on Computational | |
| Linguistics, {COLING} 2020, Barcelona, Spain (Online), December 8-13, | |
| 2020}, | |
| pages = {3285--3301}, | |
| publisher = {International Committee on Computational Linguistics}, | |
| year = {2020}, | |
| url = {https://doi.org/10.18653/v1/2020.coling-main.292}, | |
| doi = {10.18653/v1/2020.coling-main.292} | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |