Deep Learning Based Classification Using Academic Studies in Doc2Vec Model

被引：0

作者：

Safali, Yasar ^{[1
]}

Nergiz, Gozde ^{[1
]}

Avaroglu, Erdinc ^{[1
]}

Dogan, Emre ^{[2
]}

机构：

[1] Mersin Univ Bilgisayar, Muhendisligi Bolumu, Mersin, Turkey

[2] Fiat Univ, Ekobilisim Bolumu, Elazig, Turkey

来源：

2019 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND DATA PROCESSING (IDAP 2019) | 2019年

关键词：

Deep learning; Doc2vec model; text classification; LSTM; RNN; Recurrent Neural Networks; Word Embedding; Doc2vec;

D O I：

10.1109/idap.2019.8875877

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The number of academic studies published on the internet is increasing day by day. Researchers spend a long part of their time studying academic studies. They examine the harmony of their fields by looking at the title and summary of the studies. In this study, academic studies are classified based on deep learning by using Doc2vec word embeddings method. During the classification process, the studies were repeated in 9 different categories using repeated neural networks (Rnn's) and LSTM architectures.

引用

页数：5

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