Abstract: Term extraction plays an important role in ontology concept learning based on text．Because of no clear boundary among words in Chinese text,domain terms,especially compound terms,are difficult to be extracted．Traditional term extraction methods usually need large amount of calculation and lack of semantic supporting．A novel ontologyconcept learning method for compound terms was presented in this paper．At first,natural language processing technology is utilized to remove the irrelevant parts to get candidate terms．Sentences in the text are cut by punctuation marks and removed parts,so that the candidate compound terms can be reserved from wrong cutting．The candidate domain-specific terms are filtered by term frequency and information entropy with multi-strategy,according to the characteristics of distribution and statistics of terms．Then domain-specific concept set is obtained after the synonymous terms recog-nition．Experimental results show that the method can extract domain-specific word terms and compound terms with higher precision.

Key words: Term extraction,Term filtering,Compound terms,Ontology concept learning