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Clinical Named Entity Recognition (NER) is a critical task for extracting important patient information from clinical text to support clinical and translational research. This study explored the neural word embeddings derived from a large unlabeled clinical corpus for clinical NER. We systematically compared two neural word embedding algorithms and three different strategies for deriving distributed word representations. Two neural word embeddings were derived from the unlabeled Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II corpus (403,871 notes). The results from both 2010 i2b2 and 2014 Semantic Evaluation (SemEval) data showed that the binarized word embedding features outperformed other strategies for deriving distributed word representations. The binarized embedding features improved the F1-score of the Conditional Random Fields based clinical NER system by 2.3% on i2b2 data …