Improving Term Extraction Using Particle Swarm Optimization Techniques | Science Publications

Journal of Computer Science

Naomie Salim¹  Mohammad Syafrullah¹ 

关键词: Term extraction;    particle swarm optimization;    feature selection;    text mining;   

DOI  :  10.3844/jcssp.2010.323.329

学科分类：计算机科学（综合）

来源: 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.

【 授权许可】

Unknown