10. A computer-implemented method comprising: accessing positive sample entities that belong to an entity class and 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, using a computing device, 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 employing the classifier model to weight entities in a candidate dictionary to provide weightings for the entities from the candidate dictionary.
10. A computer-implemented method comprising: accessing positive sample entities that belong to an entity class and 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, using a computing device, 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 employing the classifier model to weight entities in a candidate dictionary to provide weightings for the entities from the candidate dictionary. 14. The method of claim 10 , wherein the method further comprises: clustering entities based on weighting; and generating a set of clustered dictionaries, each clustered dictionary including a cluster of entities.