Analyzing topic drift in query expansion for Information Retrieval from a large-scale patent DataBase

Topic drift has been recognized as a major reason for ineffective retrieval of documents using query expansion. Topic drift is very important in patent domain as document processing is dependable on the quality of the search. With the huge amount of available patents, we investigate on the concept of topic drift in patent domain by analyzing the topics of retrieval results obtainable with query expansion. We define and utilize topic attributes to distinguish drifting topics from other topic classes such as Vanishing, Appearing, and Rising Topics. As a result, this analysis can be used to compare two different query expansion methods for their relative effectiveness.