Predicting the Semantic Related words based on Hidden Markov Model
- 10.2991/icmmita-16.2016.161How to use a DOI?
- Hidden Markov Model; Semantic Relatedness; Natural Language Processing
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.
- © 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/).
Cite this article
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 -