Earticle

초록

영어

Generally, sequential data refers to data having continuity. Text data, which is a representative type of unstructured data, is also sequential data in that it is necessary to know the meaning of the preceding word in order to know the meaning of the following word or context. So far, many techniques for analyzing sequential data such as text data have been proposed. In this paper, four methods of 1d-CNN, LSTM, BiLSTM, and CLSTM are introduced, focusing on neural network techniques. In addition, by using this, IMDb movie review data was classified into two classes to compare the performance of the techniques in terms of accuracy and analysis time.

목차

Abstract
1. INTRODUCTION
2. NEURAL NETWORK TECHNIQUES
2.1 1-dimensional convolutional neural network
2.2 Long short-term memory
2.3 Bidirectional long short-term memory
2.4 Convolutional long short-term memory
3. COMPARISON VIA IMDB DATA
3.1 1-dimensional convolutional neural network
3.2 Long short-term memory
3.3 Bidirectional long short-term memory
3.4 Convolutional long short-term memory
4. DISCUSSION
REFERENCES

키워드

저자

• Munhee Kim [ Graduate Student, Department of Statistics, Hankuk University of Foreign Studies, Korea ]
• Kee-Hoon Kang [ Professor, Department of Statistics, Hankuk University of Foreign Studies, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700