  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.
