Choosing the most suitable classifier in a linguistic context is a well-known problem in the production of Mandarin and many other languages. The present paper proposes a solution based on BERT, compares this solution to previous neural and rule-base
d models, and argues that the BERT model performs particularly well on those difficult cases where the classifier adds information to the text.
