Online Learning Sum-Product Networks for Language Modeling

DOI

10.2991/snce-17.2017.24How to use a DOI?

Keywords

Sum-product networks; Language models; Oline learning; Deep Learning

Abstract

Sum-product networks (SPNs) have recently proposed as an remarkable representation due to their dual view as a special deep neural network with clear semantics and a probabilistic graphical model for which inference is always tractable. SPNs have been successfully applied in Computer Vision and Natural Language Processing. We used the hidden layers of SPNs to model complex dependencies among words and we used SPNs online learning algorithm to improve model learning speed and SPNs structure learning algorithm to improve modeling capabilities. Our empirical comparisons with other previous language models indicate that our online learning SPNs has better performance.

Copyright

© 2017, 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  - Zhang Yu Zhong
PY  - 2017/07
DA  - 2017/07
TI  - Online Learning Sum-Product Networks for Language Modeling
BT  - Proceedings of the 2017 7th International Conference on Social Network, Communication and Education (SNCE 2017)
PB  - Atlantis Press
SP  - 115
EP  - 120
SN  - 2352-538X
UR  - https://doi.org/10.2991/snce-17.2017.24
DO  - 10.2991/snce-17.2017.24
ID  - YuZhong2017/07
ER  -