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
this https URL

本文介绍了一个大规模的缩略语检测和提取数据集 PLOD，其中包含超过 160k 个被缩略语和长格式自动注释的文本部分，并使用该数据集生成了多个基线模型用于检测缩写词和长格式。最佳模型检测缩写词的 F1 得分为 0.92，检测其相应的长格式得分为 0.89。