In this paper, the problem of patent documents classification is considered on the basis of a extended by nominal subgroups vector representation of full-text documents. The classification process begins by extracting keywords and phrases from the documents using by means of automatic text processing. Significant of keywords ans phrases are determining according to statistical measure. The topical similarity of documents based on vectors with keywords and phrases is estimating. In this work, the three lowest levels of international patent classification are used as a set of classes
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Yadrintsev V. V. Full-text patent classification. Computer Science, Management and System Analysis. In proceedings: Scientific Conference of Young Scientists with international participation. Rostov-on-Don: Mini-Type, 2018. C. 267-274. ISBN 978-5-98615-321-6