1. One or more computer storage media storing computer-executable instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising: accessing a seed list containing positive sample entities that belong to an entity class; accessing a background entity list containing negative sample entities that do not belong to the entity class; identifying clicked URLs from search click logs for at least a portion of the positive sample entities and negative sample entities; identifying search result URLs for at least a portion of the positive sample entities and negative sample entities; identifying attributes from an entity graph for at least a portion of the positive sample entities and negative sample entities; training a classifier model using the clicked URLs, search result URLs, and attributes from the entity graph as features of the positive sample entities and negative sample entities; and using the classifier model to weight entities in a candidate dictionary to provide weightings for the entities from the candidate dictionary.
1. One or more computer storage media storing computer-executable instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising: accessing a seed list containing positive sample entities that belong to an entity class; accessing a background entity list containing negative sample entities that do not belong to the entity class; identifying clicked URLs from search click logs for at least a portion of the positive sample entities and negative sample entities; identifying search result URLs for at least a portion of the positive sample entities and negative sample entities; identifying attributes from an entity graph for at least a portion of the positive sample entities and negative sample entities; training a classifier model using the clicked URLs, search result URLs, and attributes from the entity graph as features of the positive sample entities and negative sample entities; and using the classifier model to weight entities in a candidate dictionary to provide weightings for the entities from the candidate dictionary. 2. The one or more computer storage media of claim 1 , wherein the seed list is generated based on information from at least one selected from the following: an existing entity graph and training data from a spoken language understanding system.