  Most Named Entity Recognition (NER) systems use additional features like
part-of-speech (POS) tags, shallow parsing, gazetteers, etc. Such kind of
information requires external knowledge like unlabeled texts and trained
taggers. Adding these features to NER systems have been shown to have a
positive impact. However, sometimes creating gazetteers or taggers can take a
lot of time and may require extensive data cleaning. In this paper for Chinese
NER systems, we do not use these traditional features but we use lexicographic
features of Chinese characters. Chinese characters are composed of graphical
components called radicals and these components often have some semantic
indicators. We propose CNN based models that incorporate this semantic
information and use them for NER. Our models show an improvement over the
baseline BERT-BiLSTM-CRF model. We set a new baseline score for Chinese
OntoNotes v5.0 and show an improvement of +.64 F1 score. We present a
state-of-the-art F1 score on Weibo dataset of 71.81 and show a competitive
improvement of +0.72 over baseline on ResumeNER dataset.
