Researchers have developed many computational tools aimed at extracting
collocations for both second language learners and lexicographers. Unfortunately, the tremendously large number of collocates returned by these tools usually overwhelms language learners. In this paper, we introduce a thesaurus-based semantic classification model that automatically learns semantic relations for classifying adjective-noun (A-N) and verb-noun (V-N) collocations into different thesaurus categories. Our model is based on iterative random walking over a weighted graph derived from an integrated knowledge source of word senses in WordNet and semantic categories of a thesaurus for collocation classification. We
conduct an experiment on a set of collocations whose collocates involve varying levels of abstractness in the collocation usage box of Macmillan English Dictionary. Experimental evaluation with a collection of 150 multiple-choice questions commonly used as a similarity benchmark in the TOEFL synonym test shows that a thesaurus structure is successfully imposed to help enhance collocation production for L2 learners. As a result, our methodology may improve the effectiveness of state-of-the-art collocation reference tools concerning the aspects of language understanding and learning, as well as lexicography.