论文标题
基于深度学习的方法，用于自动检测壳名词和Wikitext-2评估
Deep Learning-based approaches for automatic detection of shell nouns and evaluation on WikiText-2
论文作者
论文摘要
在某些领域，例如认知语言学，研究人员仍在使用基于手动规则和模式的传统技术。由于Shell名词的定义相当主观，而且有很多例外，因此，当深度学习技术还不够成熟时，必须手工完成这项耗时的工作。随着网络语言数量的越来越多，这些规则变得不那么有用。但是，现在有更好的选择。随着深度学习的发展，预训练的语言模型为自然语言处理提供了良好的技术基础。基于深度学习方法的自动化过程更符合现代需求。本文在跨边界合作，提出了两个神经网络模型，以自动检测Shell名词和Wikitext-2数据集上的实验。提出的方法不仅允许整个过程自动化，而且即使在完全看不见的文章中，精度也达到了94％，与人类注释者相当。这表明该模型的性能和概括能力足以用于研究目的。发现许多新名词非常适合壳名词的定义。 GitHub上都可以使用所有发现的外壳名词以及预训练的模型和代码。
In some areas, such as Cognitive Linguistics, researchers are still using traditional techniques based on manual rules and patterns. Since the definition of shell noun is rather subjective and there are many exceptions, this time-consuming work had to be done by hand in the past when Deep Learning techniques were not mature enough. With the increasing number of networked languages, these rules are becoming less useful. However, there is a better alternative now. With the development of Deep Learning, pre-trained language models have provided a good technical basis for Natural Language Processing. Automated processes based on Deep Learning approaches are more in line with modern needs. This paper collaborates across borders to propose two Neural Network models for the automatic detection of shell nouns and experiment on the WikiText-2 dataset. The proposed approaches not only allow the entire process to be automated, but the precision has reached 94% even on completely unseen articles, comparable to that of human annotators. This shows that the performance and generalization ability of the model is good enough to be used for research purposes. Many new nouns are found that fit the definition of shell noun very well. All discovered shell nouns as well as pre-trained models and code are available on GitHub.
