Automatic refinement of patent queries using concept importance predictors
Parvaz Mahdabi, Linda Andersson, Mostafa Keikha, Fábio Crestani · 2012
Patent prior art queries are full patent applications which are much longer than standard web search topics. Such queries are composed of hundreds of terms and do not represent a focused information need. One way to make the queries more focused is to select a group of key terms as representatives. Existing works show that such a selection to reduce patent queries is a challenging task mainly because of the presence of ambiguous terms. Given this setup, we present a query modeling approach where we utilize patent-specific characteristics to generate more precise queries. We propose to automatically disambiguate query terms by employing noun phrases that are extracted using the global analysis of the patent collection. We further introduce a method for predicting whether expansion using noun phrases would improve the retrieval effectiveness.