Fast and Accurate Patent Classification in Search Engines

This article presents a new approach to large scale patent classification. The need to classify documents often takes place in professional information retrieval systems. In this paper we describe our approach, based on linguistically-supported k-nearest neighbors. We experimentally evaluate it on t...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1117; no. 1; pp. 12004 - 12009
Main Authors Yadrintsev, Vasiliy, Bakarov, Amir, Suvorov, Roman, Sochenkov, Ilya
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.11.2018
Subjects
Classification
Information retrieval
Search engines
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Summary:This article presents a new approach to large scale patent classification. The need to classify documents often takes place in professional information retrieval systems. In this paper we describe our approach, based on linguistically-supported k-nearest neighbors. We experimentally evaluate it on the Russian and English datasets and compare modern classification technique fastText. We show that KNN is a viable alternative to traditional text classifiers, achieving comparable accuracy while using less additional hardware resources.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1117/1/012004