The Hybrid Method for Accurate Patent Classification
- Авторы: Yadrintsev V.1,2, Sochenkov I.1,3
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Учреждения:
- Federal Research Center Computer Science and Control of the Russian Academy of Sciences
- Peoples’ Friendship University of Russia (RUDN University)
- Lomonosov Moscow State University
- Выпуск: Том 40, № 11 (2019)
- Страницы: 1873-1880
- Раздел: Article
- URL: https://journals.rcsi.science/1995-0802/article/view/206101
- DOI: https://doi.org/10.1134/S1995080219110325
- ID: 206101
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Аннотация
This article is dedicated to stacking of two approaches of patent classification. First is based on linguistically-supported k-nearest neighbors algorithm using the method of search for topically similar documents based on a comparison of vectors of lexical descriptors. Second is the word embeddings based fastText, where the sentence (or a document) vector is obtained by averaging the n-gram embeddings, and then a multinomial logistic regression exploits these vectors as features. We show in Russian and English datasets that stacking classifier shows better results compared to single classifiers.
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Об авторах
V. Yadrintsev
Federal Research Center Computer Science and Control of the Russian Academy of Sciences; Peoples’ Friendship University of Russia (RUDN University)
Автор, ответственный за переписку.
Email: vvyadrincev@gmail.com
Россия, Moscow, 119333; Moscow, 117198
I. Sochenkov
Federal Research Center Computer Science and Control of the Russian Academy of Sciences; Lomonosov Moscow State University
Автор, ответственный за переписку.
Email: sochenkov@isa.ru
Россия, Moscow, 119333; Moscow, 119991