Named entity lists provide important features for language understanding, but typical lists can contain many ambiguous or incorrect phrases. We present an approach for automatically learning weighted entity lists by mining user clicks from web search logs. The approach significantly outperforms multiple baseline approaches and the weighted lists improve spoken language understanding tasks such as domain detection and slot filling. Our methods are general and can be easily applied to large quantities of entities, across any number of lists.