language: en

Multiple Retrieval Models and Regression Models for Prior Art Search

Patrice Lopez * and Laurent Romary ?
Humboldt Universität zu Berlin - Institut für Deutsche Sprache und Linguistik ? INRIA - Gemo Research Group * European Patent Office,
Berlin
patrice_lopez@hotmail.com laurent.romary@loria.fr

Abstract This paper presents the system called PATATRAS (PATent and Article Tracking, Retrieval and AnalysiS) 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 (KL, Okapi) and term index definitions (lemma, phrase, concept) for the three languages considered in the present track (English, French, German) producing ten different sets of ranked results. 2. The merging of the different results based on multiple regression models using an additional validation set created from the patent collection. 3. The exploitation of patent metadata and of the citation structures for creating restricted initial working sets of patents and for producing a final re-ranking regression model.As we exploit specific metadata of the patent documents and the citation relations only at the creation of initial working sets and during the final post ranking step, our architecture remains generic and easy to extend.

H.3 [Information Storage and Retrieval]: H.3.1 Content Analysis and Indexing; H.3.3 Information Search and Retrieval; H.3.4 Systems and Software; H.3.7 Digital Libraries; H.2.3 [Database Managment]: Languages?Query Languages General Terms Measurement, Performance, Experimentation Keywords Patent, Prior Art Search, Multilinguality, Regression models, Re-ranking

1 Motivation