Automatic Indexing of Patent Right-claiming Document Based on Deep Learning

DOI

10.2991/ammsa-18.2018.28How to use a DOI?

Keywords

deep learning; word2vec; k-means; automatic indexing; keyword extraction

Abstract

In recent years, there have been more and more applications of deep learning in natural language processing, and people have paid more and more attention to the value embodied in patent. In this paper, based on the value of right-claiming document in the patent, a deep learning tool word2vec is used to convert text information into a set of word embeddings. The word embeddings carry semantic information, so that the quantified metrics the relationships between words. Then, the k-means clustering method is used to extract words whose distance between words is closer to the center of the cluster, so as to achieve the purpose of automatic indexing the right-claiming document.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qinghong Zhong
AU  - Xiaodong Qiao
AU  - Yunliang Zhang
PY  - 2018/05
DA  - 2018/05
TI  - Automatic Indexing of Patent Right-claiming Document Based on Deep Learning
BT  - Proceedings of the 2018 2nd International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2018)
PB  - Atlantis Press
SP  - 135
EP  - 139
SN  - 1951-6851
UR  - https://doi.org/10.2991/ammsa-18.2018.28
DO  - 10.2991/ammsa-18.2018.28
ID  - Zhong2018/05
ER  -