A Multilingual Patent Text-Mining Approach for Computing Relatedness Evaluation of Patent Documents

This paper describes our work on developing a language-independent technique for discovery of implicit knowledge about patents from multilingual patent information sources. Traditional techniques of multi- and cross-language patent retrieval are mostly based on the process of translation. One major problem of those is that it is difficult to find related patents produced from other countries in a stand-alone patent information system. In this paper, we present a novel system platform to support locating similar and relevant multilingual patent documents. The platform is developed using a multilingual vector space based on the latent semantic indexing (LSI) model, and utilizing collected professional Chinese-English parallel corpora for training the system model. These multilingual patent documents can then be mapped into the semantic vector space for evaluating their similarity by means of text clustering techniques. The preliminary results show that our platform framework has potential for retrieval and relatedness evaluation of multilingual patent documents.