1. A computer system for iterative set expansion using samples, the system comprising: a processor and memory configured to execute software instructions embodied within the following components; an input component that receives a set of seed terms and a set of terms and associated contexts with which to expand the set of seed terms; a data modeling component that models the received terms and seeds as a bipartite graph with candidate terms being nodes on one side and identified context nodes on the other side, and models web query log data by dividing each query into a context of a fixed number of tokens of prefix or suffix and a remaining term; a similarity determining component that determines a similarity metric between two candidate nodes in the graph based on the candidate nodes' relationship to the context nodes in the graph; a relevance determining component that determines a relevance metric that indicates how similar a node in the graph is to the received seed terms and corresponding nodes; a coherence determining component that determines a coherence metric that indicates how consistent a concept set is that includes the seed terms and one or more candidate nodes; a quality measurement component that combines the determined relevance metric and coherence metric to determine a quality metric that indicates relevance and coherence among a set of nodes in the graph; an iterative expansion component that identifies an expanded seed set having a high quality metric; and a set reporting component that reports the identified expanded seed set as output.
1. A computer system for iterative set expansion using samples, the system comprising: a processor and memory configured to execute software instructions embodied within the following components; an input component that receives a set of seed terms and a set of terms and associated contexts with which to expand the set of seed terms; a data modeling component that models the received terms and seeds as a bipartite graph with candidate terms being nodes on one side and identified context nodes on the other side, and models web query log data by dividing each query into a context of a fixed number of tokens of prefix or suffix and a remaining term; a similarity determining component that determines a similarity metric between two candidate nodes in the graph based on the candidate nodes' relationship to the context nodes in the graph; a relevance determining component that determines a relevance metric that indicates how similar a node in the graph is to the received seed terms and corresponding nodes; a coherence determining component that determines a coherence metric that indicates how consistent a concept set is that includes the seed terms and one or more candidate nodes; a quality measurement component that combines the determined relevance metric and coherence metric to determine a quality metric that indicates relevance and coherence among a set of nodes in the graph; an iterative expansion component that identifies an expanded seed set having a high quality metric; and a set reporting component that reports the identified expanded seed set as output. 4. The system of claim 1 wherein the coherence determining component considers similarity of nodes to other candidate nodes to identify nodes that are relevant but are identified as not likely to belong in the same expanded set because they are incoherent compared to other candidate nodes.