文章基本信息

标题：Improving Term Extraction Using Particle Swarm Optimization Techniques
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作者：Mohammad Syafrullah ; Naomie Salim
期刊名称：Journal of Computing
电子版ISSN：2151-9617
出版年度：2010
卷号：2
期号：2
出版社：Journal of Computing
摘要：Term extraction is one of the layers in the ontology development process which has the task to extract all the terms contained in the input document automatically. The purpose of this process is to generate list of terms that are relevant to the domain of the input document. In the literature there are many approaches, techniques and algorithms used for term extraction. In this paper we propose a new approach using particle swarm optimization techniques in order to improve the accuracy of term extraction results. We choose five features to represent the term score. The approach has been applied to the domain of religious document. We compare our term extraction method precision with TFIDF, Weirdness, GlossaryExtraction and TermExtractor. The experimental results show that our propose approach achieve better precision than those four algorithm