Datasets:acronym_identification

 --- annotations_creators: - expert-generated language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 10K 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.