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Noun phrases extraction using shallow parsing with C4.5 decision tree algorithm for Indonesian Language ontology building

机译:用C4.5决策树算法使用浅析,使用浅析浅析印度尼西亚语言本体建设

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Ontology describes a set of concept or entity and each relation. Ontology as knowledge representation usually has a large structure because it can cover a wide area topics. Ontology building process is divided into two subprocesses, those are term extraction and relation formation. Term extraction in ontology building is done for extracting concept or entity before each relation is obtained. Main objective in this research is to extract noun phrases using shallow parsing algorithm based on C4.5 decision tree as candidate concept or term for ontology building process in Indonesian Text. One of the advantages of using shallow parsing is it can recover syntactic information efficiently and reliably from unrestricted text. For our dataset, we use Indonesian Language online newspapers for one month. Based on our experiments, it concludes that our proposed method can perform well for Indonesian Language noun phrase identification with average F-score 84.63%.
机译:本体描述了一组概念或实体和每个关系。作为知识表示的本体论通常具有大结构,因为它可以涵盖广域的主题。本体建设过程分为两个子过程,其中是术语提取和关系形成。在获得每个关系之前,在获得概念或实体之前完成本体建设中的术语提取。本研究的主要目标是使用基于C4.5决策树的浅解析算法提取名词短语作为印度尼西亚语文本中本体构建过程的候选概念或术语。使用浅析解析的其中一个优点是它可以从未受到限制的文本有效且可靠地恢复句法信息。对于我们的数据集,我们使用印度尼西亚语言在线报纸一个月。基于我们的实验,它的结论是,我们所提出的方法对于印度尼西亚语言词组短语识别,平均F分数为84.63%。

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