6. A computer-implemented method to expand a set of seeds while applying a dynamic threshold of relatedness, the method comprising: receiving from a web query log a set of terms with contexts and one or more identified seeds, wherein the seeds are terms that are related to a concept for which to identify additional related terms from the set of terms; modeling 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 by dividing each query into a context of a fixed number of tokens of prefix or suffix and a remaining term; determining a relevance score for each term based on the identified seeds; ranking the received set of terms by the determined relevance score; selecting an initial threshold ranking value for separating terms in the set related to the seeds from terms not related to the seeds; picking a top ranked number of terms above a threshold from the ranked set of terms based on the selected initial threshold to form a new set; determining a quality measurement that identifies how well each term relates to the picked threshold number of terms in the new set; ranking the terms in the new set based on the determined quality measurement; selecting a next threshold to use to separate terms in the new set related to the seeds from terms not related to the seeds; using the selected next threshold to select a threshold number of terms from the ranked new set; repeating the steps of determining the quality measurement, ranking the terms, and selecting a threshold number of terms for a determined number of iterations; and reporting the resulting expanded seed set that includes the terms in the received set that are the highest quality matches to the received seeds, wherein the preceding steps are performed by at least one processor.
6. A computer-implemented method to expand a set of seeds while applying a dynamic threshold of relatedness, the method comprising: receiving from a web query log a set of terms with contexts and one or more identified seeds, wherein the seeds are terms that are related to a concept for which to identify additional related terms from the set of terms; modeling 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 by dividing each query into a context of a fixed number of tokens of prefix or suffix and a remaining term; determining a relevance score for each term based on the identified seeds; ranking the received set of terms by the determined relevance score; selecting an initial threshold ranking value for separating terms in the set related to the seeds from terms not related to the seeds; picking a top ranked number of terms above a threshold from the ranked set of terms based on the selected initial threshold to form a new set; determining a quality measurement that identifies how well each term relates to the picked threshold number of terms in the new set; ranking the terms in the new set based on the determined quality measurement; selecting a next threshold to use to separate terms in the new set related to the seeds from terms not related to the seeds; using the selected next threshold to select a threshold number of terms from the ranked new set; repeating the steps of determining the quality measurement, ranking the terms, and selecting a threshold number of terms for a determined number of iterations; and reporting the resulting expanded seed set that includes the terms in the received set that are the highest quality matches to the received seeds, wherein the preceding steps are performed by at least one processor. 8. The method of claim 6 wherein receiving the terms and seeds comprises receiving the set of terms from a list retrieved from the Internet.