Application of Deep Learning in Text Mining
Haoriqin Wang, Mingyang Jiang, Jianhong Qi, Xinhong Zhang, Qinghu Wang, Yuxin Zhou, Mingyu Bai, Lisha Liu, Zhili Pei
Available Online March 2014.
- https://doi.org/10.2991/mce-14.2014.80How to use a DOI?
- deep learning; text mining; characteristics and applications; network technology; machine learning
- With the advancement of science and technology development and the development of social , more and more current information technology and computer technology has been used in the various aspects of life and work, the current rapid development of network technology has penetrated into all aspects of life. With the development of network technology and the text messaging of network gradually strengthened, wide and messy network information, in the vast network of information, how to efficiently access the information that we need quickly, which is related to the text mining technologies. The deep learning is a new learning method of the machine learning, it simulates the human brain and analysis the neural network through the imitation of the human brain and the interpretation of the relevant data. In the text mining, the application of the deep learning can be a very good text clustering and text classification, it is easy to find the desired text information, so the application of the deep learning plays an important role in the deep learning. Research for this paper analyzes the deep learning in text mining and the related content of knowledge.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Haoriqin Wang AU - Mingyang Jiang AU - Jianhong Qi AU - Xinhong Zhang AU - Qinghu Wang AU - Yuxin Zhou AU - Mingyu Bai AU - Lisha Liu AU - Zhili Pei PY - 2014/03 DA - 2014/03 TI - Application of Deep Learning in Text Mining BT - 2014 International Conference on Mechatronics, Control and Electronic Engineering (MCE-14) PB - Atlantis Press SP - 361 EP - 364 SN - 1951-6851 UR - https://doi.org/10.2991/mce-14.2014.80 DO - https://doi.org/10.2991/mce-14.2014.80 ID - Wang2014/03 ER -