  The detection and extraction of abbreviations from unstructured texts can
help to improve the performance of Natural Language Processing tasks, such as
machine translation and information retrieval. However, in terms of publicly
available datasets, there is not enough data for training
deep-neural-networks-based models to the point of generalising well over data.
This paper presents PLOD, a large-scale dataset for abbreviation detection and
extraction that contains 160k+ segments automatically annotated with
abbreviations and their long forms. We performed manual validation over a set
of instances and a complete automatic validation for this dataset. We then used
it to generate several baseline models for detecting abbreviations and long
forms. The best models achieved an F1-score of 0.92 for abbreviations and 0.89
for detecting their corresponding long forms. We release this dataset along
with our code and all the models publicly in
https://github.com/surrey-nlp/PLOD-AbbreviationDetection
