Using Negative Information in Search

Consider a user searching for information on the World Wide Web. If the information need of the user is somewhat specific, and if the user is permitted to provide a detailed description of his precise need, then it is quite likely that this description will include negative constraints, i.e., specifications of what the user is ´not´ looking for. A search engine that makes use of such constraints is likely to return more accurate results. In this paper, we consider the problem of identifying such negative constraints from verbose queries. A maximum-entropy classifier is trained to identify negative sentences in verbose queries with about 90% accuracy. We next study how retrieval effectiveness is affected when these negative sentences are eliminated from the queries. We find that this step results in modest improvements in retrieval accuracy, but our analysis suggests that significant improvements can be obtained if negative sentences are properly handled during query processing.