文章基本信息

标题：Improving Term Extraction Using Particle Swarm Optimization Techniques
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作者：Syafrullah, Mohammad ; Salim, Naomie
期刊名称：Journal of Computer Science
印刷版ISSN：1549-3636
出版年度：2010
卷号：6
期号：3
页码：323-329
DOI：10.3844/jcssp.2010.323.329
出版社：Science Publications
摘要：Problem statement: 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 where each of approaches, techniques and algorithms has the objective to improve the precision of the extracted terms. Approach: We proposed a new approach using particle swarm optimization techniques in order to improve the precision of term extraction results. We choose five features to represent the term score. Results: The approach had been applied to the domain of Islamic documents. We compare our term extraction method with TFIDF, Weirdness, GlossaryExtraction and TermExtractor. Conclusion: The experimental results showed that our proposed approach achieves better precision than those four algorithms.
关键词：Term extraction; particle swarm optimization; feature selection; text mining