Most previous work of text normalization on informal text made a strong assumption that the system has already known which tokens are non-standard words (NSW) and thus need normalization. However, this is not realistic. In this paper, we propose a method for NSW detection. In addition to the information based on the dictionary, e.g., whether a word is out-of-vocabulary (OOV), we leverage novel information derived from the normalization results for OOV words to help make decisions. Second, this paper investigates two methods using NSW detection results for named entity recognition (NER) in social media data. One adopts a pipeline strategy, and the other uses a joint decoding fashion. We also create a new data set with newly added normalization annotation beyond the existing named entity labels. This is the first data set with such annotation and we release it for research purpose. Our experiment results demonstrate the effectiveness of our NSW detection method and the benefit of NSW detection for NER. Our proposed methods perform better than the state-of-the-art NER system.