2. An article as in claim 1 , wherein the symbols are allocated to the bins by using association statistics to map the symbols to points in an information geometric space and to assign near points to the same bin.
2. An article as in claim 1 , wherein the symbols are allocated to the bins by using association statistics to map the symbols to points in an information geometric space and to assign near points to the same bin. 6. An article as in claim 2 , wherein the query includes multiple sub-queries, and wherein the reasoning comprises: generating a candidate response for each sub-query; determining a degree of clustering of symbols associated with each sub-query; and searching symbols within the information geometric space for the sub-query having the highest degree of clustering of symbols prior to searching symbols within the information geometric space for the other sub-queries.