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Word sense disambiguation (WSD) is one of tricky tasks in natural language processing (NLP) as it needs to take into full account all the complexities of language. Because WSD involves in discovering semantic structures from unstructured text, automatic knowledge acquisition of word sense is profoundly difficult. To acquire knowledge about Chinese multi-sense verbs, we introduce an incremental machine learning method which combines rough set method and instance based learning. First, context ofdoi:10.4108/sis.2.5.e3 fatcat:73qnoasqdbfolmfhp24q43fbga