  Compared with English, Chinese suffers from more grammatical ambiguities,
like fuzzy word boundaries and polysemous words. In this case, contextual
information is not sufficient to support Chinese named entity recognition
(NER), especially for rare and emerging named entities. Semantic augmentation
using external knowledge is a potential way to alleviate this problem, while
how to obtain and leverage external knowledge for the NER task remains a
challenge. In this paper, we propose a neural-based approach to perform
semantic augmentation using external knowledge from search engine for Chinese
NER. In particular, a multi-channel semantic fusion model is adopted to
generate the augmented input representations, which aggregates external related
texts retrieved from the search engine. Experiments have shown the superiority
of our model across 4 NER datasets, including formal and social media language
contexts, which further prove the effectiveness of our approach.
