1. A method for document analysis, comprising the steps of: designating a subset of relevant documents from a document collection; using a greedy algorithm to establish a query coverage set of words or terms, wherein at each stage thereof a single word or term from the subset of relevant documents is included in the query coverage set, wherein the single word or term minimizes a distance measurement between the document collection and the query coverage set, wherein the distance measurement is determined by constructing a difficulty model for a topic by computing a plurality of distances comprising a first distance between the query coverage set and the document collection (d(Q,C)), a second distance among the query coverage set (d(Q,Q)); a third distance between the subset of relevant documents and the document collection (d(R,C)), a fourth distance among the subset of relevant documents (d(R,R)), and a fifth distance between the query coverage set and the subset of relevant documents (d(Q,R)); storing the query coverage set in a database; constructing a set of queries from the query coverage set, each of the queries having a number of terms; executing the queries in a search engine to generate respective results; responsively to the respective results determining an average precision for each of the queries by considering the subset of relevant documents as representing the document collection; categorizing the queries by analyzing the average precision against the number of terms thereof; and reporting respective abilities of the categorized queries to find information in the subset of relevant documents.
1. A method for document analysis, comprising the steps of: designating a subset of relevant documents from a document collection; using a greedy algorithm to establish a query coverage set of words or terms, wherein at each stage thereof a single word or term from the subset of relevant documents is included in the query coverage set, wherein the single word or term minimizes a distance measurement between the document collection and the query coverage set, wherein the distance measurement is determined by constructing a difficulty model for a topic by computing a plurality of distances comprising a first distance between the query coverage set and the document collection (d(Q,C)), a second distance among the query coverage set (d(Q,Q)); a third distance between the subset of relevant documents and the document collection (d(R,C)), a fourth distance among the subset of relevant documents (d(R,R)), and a fifth distance between the query coverage set and the subset of relevant documents (d(Q,R)); storing the query coverage set in a database; constructing a set of queries from the query coverage set, each of the queries having a number of terms; executing the queries in a search engine to generate respective results; responsively to the respective results determining an average precision for each of the queries by considering the subset of relevant documents as representing the document collection; categorizing the queries by analyzing the average precision against the number of terms thereof; and reporting respective abilities of the categorized queries to find information in the subset of relevant documents. 10. The method according to claim 1 , further comprising the steps of: partitioning the document collection into a plurality of domains; for each of the domains performing the steps of designating a subset of relevant documents, using a greedy algorithm, storing the query coverage set, constructing a set of queries, and executing the queries; determining an average overlap among the terms in the queries at a plurality of cut-off points; and determining from the average overlap the most similar domains.