Eko Mulyanto Yuniarno, Mochamad Hariadi, Mauridhi Hery Purnomo : Noun Phrases Extraction Using Shallow Parsing with C4.5 Decision Tree Algorithm for Indonesian Language Ontology Building

Eko Mulyanto Yuniarno S.T., M.T.
Mochamad Hariadi ST., M.Sc., Ph.D
Prof.Dr.Ir. Mauridhi Hery Purnomo M.Eng.



Published in

2015 15th International Symposium On Communication and Information Technologies(ISCIT)

External link


Seminar Internasional



Abstract—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 ef?ciently 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 identi?cation with average F-score 84.63%. Keywords-Noun Phrase; Term Extraction; Shallow Parsing; Indonesian Language; Decision Tree; Data Mining