Deep Learning Based Classification Using Academic Studies in Doc2Vec Model - Safali, Yasar; Nergiz, Gozde; Avaroglu, Erdinc; Dogan, Emre (Institute of Electrical and Electronics Engineers Inc., 2019)
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. © 2019 IEEE.
Turkçe Akademik Calismalarin Doc2Vec Modeli Kullanilarak Derin Ögrenme Tabanli Siniflandirilmasi
2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019 -- 21 September 2019 through 22 September 2019 -- Malatya -- 153040
Keywords: Deep learning; Doc2vec; Doc2vec model; LSTM; Recurrent Neural Networks; RNN; text classification; Word Embedding