Semantic Knowledge Acquisition based on Maximum Entropy

DOI

10.2991/mecae-17.2017.62How to use a DOI?

Keywords

Semantic Knowledge; Maximum Entropy; Semantic Distance.

Abstract

It's necessary to acquire semantic knowledge in Natural Language Processing research. In this paper, we present an approach for acquiring Chinese semantic knowledge based on maximum entropy model. Semantic knowledge units are composed of central word and a group of feature words. Because the maximum entropy to extract features, we utilize it to calculate the semantic distance between the central word and feature words in large-scale network corpus. In the experiment, tests on a number of manual defined data sets show that the Spearman correlation coefficient has been increased 6.2%-20.9%.

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  - Maoyuan Zhang
AU  - Kai Xing
AU  - Jianping Zhu
PY  - 2017/03
DA  - 2017/03
TI  - Semantic Knowledge Acquisition based on Maximum Entropy
BT  - Proceedings of the 2017 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2017)
PB  - Atlantis Press
SP  - 334
EP  - 337
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-17.2017.62
DO  - 10.2991/mecae-17.2017.62
ID  - Zhang2017/03
ER  -