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Semantic Knowledge Acquisition based on Maximum Entropy

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成果类型:
会议论文
作者:
Zhang, Maoyuan*;Xing, Kai;Zhu, Jianping
通讯作者:
Zhang, Maoyuan
作者机构:
[Zhu, Jianping; Xing, Kai; Zhang, Maoyuan] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.
通讯机构:
[Zhang, Maoyuan] C
Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.
语种:
英文
关键词:
Semantic Knowledge;Maximum Entropy;Semantic Distance
期刊:
PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON MECHANICAL, ELECTRONIC, CONTROL AND AUTOMATION ENGINEERING (MECAE 2017)
ISSN:
2352-5401
年:
2017
卷:
61
页码:
334-337
会议名称:
International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE)
会议论文集名称:
AER-Advances in Engineering Research
会议时间:
MAR 25-26, 2017
会议地点:
Beijing, PEOPLES R CHINA
会议主办单位:
[Zhang, Maoyuan;Xing, Kai;Zhu, Jianping] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.
主编:
Ke, J Xiao, J Davis, H
出版地:
29 AVENUE LAVMIERE, PARIS, 75019, FRANCE
出版者:
ATLANTIS PRESS
ISBN:
978-94-6252-315-9
基金类别:
Humanity and Social Science Youth Foundation of Ministry of Education of China [15YJC870029]; self-determined research funds of CCNU from the colleges' basic research and operation of MOE [CCNU16A02049, CCNU16A06039]
机构署名:
本校为第一且通讯机构
院系归属:
计算机学院
摘要:
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 Spe...

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