The success of syndromic surveillance depends on the ability of the surveillance community to quickly and accurately recognize anomalous data. Current methods of anomaly detection focus on sets of syndromic categories and rely on a priori knowledge to map chief complaints to these general syndromic categories. As a result, the mapping scheme may miss key terms and phrases that have not previously been used. Furthermore, analysts do not have a good way of being alerted to these new terms in order to determine if they should be added to the syndromic mapping schema. We use a dynamic dictionary of terms to side-step the downfalls of a priori knowledge in this rapidly evolving field by alerting the analyst to rare and brand new words used in the chief complaint field.