Towards patent text analysis based on semantic role labelling

Abstract

Mining patent texts can obtain valuable technical information and competitive intelligence which is important for the development of technology and business. The current patent text mining approaches suffer from lack of effective, automatic, accurate and wide-coverage techniques that can annotate natural language texts with semantic argument structure. It is helpful for text mining to derive more meaningful semantic relationship from semantic role labelling (SRL) results of patents. This paper uses Word2Vec to learn word real-valued vector and design features related to word vector to train SRL parser. Based on the SRL parser, two patent text mining methods are then given: patent topic extraction and automatic construction of patent technical effect matrix (PTEM). Experiments show that semantic role labelling help achieve satisfactory results and saves manpower.