PATATRAS: Retrieval model combination and regression models for prior art search

Abstract

This paper presents PATATRAS (PATent and Article Tracking, Retrieval and AnalysiS), a system realized at the Humboldt University for the IP track of CLEF 2009. Our approach presents three main characteristics: 1 The usage of multiple retrieval models and term index definitions for the three languages considered in the present track producing ten different sets of ranked results. 1 The merging of the different results based on multiple regression models using an additional training set created from the patent collection. 1 The exploitation of patent metadata and the citation structures for creating restricted initial working sets of patents and for producing a final re-ranking regression model. The resulting architecture allowed us to exploit efficiently specific information of patent documents while remaining generic and easy to extend. © 2010 Springer-Verlag Berlin Heidelberg.