  Identifying the named entities mentioned in text would enrich many semantic
applications at the downstream level. However, due to the predominant usage of
colloquial language in microblogs, the named entity recognition (NER) in
Chinese microblogs experience significant performance deterioration, compared
with performing NER in formal Chinese corpus. In this paper, we propose a
simple yet effective neural framework to derive the character-level embeddings
for NER in Chinese text, named ME-CNER. A character embedding is derived with
rich semantic information harnessed at multiple granularities, ranging from
radical, character to word levels. The experimental results demonstrate that
the proposed approach achieves a large performance improvement on Weibo dataset
and comparable performance on MSRA news dataset with lower computational cost
against the existing state-of-the-art alternatives.
