Human labeled corpus is indispensable for the training of supervised word segmenters. However, it is time-consuming and labor-intensive to label corpus manually. During the process of typing Chinese text by Pingyin, people usually need to type "space" or nu-meric keys to choose the words due to homo-phones, which can be viewed as a cue for segmentation. We argue that such a process can be used to build a labeled corpus in a more natural way. Thus, in this paper, we in-vestigate Natural Typing Annotations (NTAs) that are potential word delimiters produced by users while typing Chinese. A detailed analysis on over three hundred user-produced texts containing NTAs reveals that high-quality NTAs mostly agree with gold segmentation and, consequently, can be used for improving the performance of supervised word segmentation model in out-of-domain. Experiments show that a classification model combined with a voting mechanism can reli-ably identify the high-quality NTAs texts that are more readily available labeled corpus. Furthermore, the NTAs might be particularly useful to deal with out-of-vocabulary (OOV) words such as proper names and neo-logisms.