论文标题
PLOD：科学文档的缩写检测数据集
PLOD: An Abbreviation Detection Dataset for Scientific Documents
论文作者
论文摘要
从非结构化文本中检测和提取缩写可以帮助提高自然语言处理任务的性能，例如机器翻译和信息检索。但是，就公开可用的数据集而言，没有足够的数据来培训基于深神经网络的模型，以使其对数据进行良好的概括。本文介绍了PLOD，这是一种大规模数据集，用于缩写检测和提取，其中包含160K+段自动注释，并以缩写及其长形式注释。我们对一组实例进行了手动验证，并对此数据集进行了完整的自动验证。然后，我们使用它来生成几种基线模型来检测缩写和长形式。最佳模型的缩写为0.92，检测其相应的长形式为0.89。我们在https://github.com/surrey-nlp/plod-abbreviationdetection中公开发布此数据集以及我们的代码和所有模型
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
