Enhancing Patent Expertise through Automatic Matching with Scientific Papers

Abstract

International audienceThis paper focuses on a subtask of the QUAERO1 research program, a major innovating research project related to the automatic processing of multimedia and multilingual content. The objective discussed in this article is to propose a new method for the classification of scientific papers, developed in the context of an international patents classification plan related to the same field. The practical purpose of this work is to provide an assistance tool to experts in their task of evaluation of the originality and novelty of a patent, by offering to the latter the most relevant scientific citations. This issue raises new challenges in categorization research as the patent classification plan is not directly adapted to the structure of scientific documents, classes have high citation or cited topic and that there is not always a balanced distribution of the available examples within the different learning classes. We propose, as a solution to this problem, to apply an improved K-nearest-neighbors (KNN) algorithm based on the exploitation of association rules occurring between the index terms of the documents and the ones of the patent classes. By using a reference dataset of patents belonging to the field of pharmacology, on the one hand, and a bibliographic dataset of the same field issued from the Medline collection, on the other hand, we show that this new approach, which combines the advantages of numerical and symbolical approaches, improves considerably categorization performance, as compared to the usual categorization methods

A New Feature Selection and Feature Contrasting Approach Based on Quality Metric: Application to Efficient Classification of Complex Textual Data

“…A common solution to deal with unbalance in dataset is undersampling majority classes and oversampling minority classes. However, sampling that introduces redundancy in dataset does not improve the performance in this dataset, as it has been shown in [12]. We thus propose hereafter to prune irrelevant features and to contrast the relevant ones as an alternative solution.…”

“…The source data contains 6387 patents in XML format, grouped into 15 subclasses of the A61K class (medical preparation). 25887 citations have been extracted from 6387 patents [12]. Then the Medline database is queried with extracted citations for related scientific articles.…”

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