  Hypernym identification of open-domain entities is crucial for taxonomy
construction as well as many higher-level applications. Current methods suffer
from either low precision or low recall. To decrease the difficulty of this
problem, we adopt a classification-based method. We pre-define a concept
taxonomy and classify an entity to one of its leaf concept, based on the name
and description information of the entity. A convolutional neural network
classifier and a K-means clustering module are adopted for classification. We
applied this system to 2.1 million Baidu Baike entities, and 1.1 million of
them were successfully identified with a precision of 99.36%.
