Predicting the Semantic Related words based on Hidden Markov Model

DOI

10.2991/icmmita-16.2016.161How to use a DOI?

Keywords

Hidden Markov Model; Semantic Relatedness; Natural Language Processing

Abstract

This paper presents a method of predicting the words with semantic relation based on Hidden Markov Model (HMM). Two words are set as an observation sequence, combined with HMM and the corpus, which has taken some works in Natural Language Processing, to calculate the most probable sequence with semantic relation by the given observation sequence. By Reducing the impact of high frequency words on the traditional method of semantic prediction based on the Text-window Co-occurrence. The experiment results show that this method is effective.

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/).

Download article (PDF)

TY  - CONF
AU  - Fuping Yang
AU  - Huafeng Gu
PY  - 2017/01
DA  - 2017/01
TI  - Predicting the Semantic Related words based on Hidden Markov Model
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 865
EP  - 871
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.161
DO  - 10.2991/icmmita-16.2016.161
ID  - Yang2017/01
ER  -