  Fine-grained entity typing is a challenging task with wide applications.
However, most existing datasets for this task are in English. In this paper, we
introduce a corpus for Chinese fine-grained entity typing that contains 4,800
mentions manually labeled through crowdsourcing. Each mention is annotated with
free-form entity types. To make our dataset useful in more possible scenarios,
we also categorize all the fine-grained types into 10 general types. Finally,
we conduct experiments with some neural models whose structures are typical in
fine-grained entity typing and show how well they perform on our dataset. We
also show the possibility of improving Chinese fine-grained entity typing
through cross-lingual transfer learning.
