A new approach of rules extraction for word sense disambiguation by features of attributes

Abstract

Classification is an important issue in data mining and knowledge discovery. It is a significant issue to develop effective and easy approach of rule extraction for classification. A new approach of rule extraction by features of attributes is proposed in this article for word sense disambiguation (WSD). English preposition on is taken as a target word of WSD, a data set of 600 samples is randomly selected from a 350,000 words corpus. Semantic and syntactic features are extracted from the context, and the corresponding formal context is generated. The rules for WSD of English preposition on are extracted based on the theoretical descriptions and calculation of the simple class exclusive attributes and composite class exclusive attributes. The extracted rules are used in the WSD of English preposition on, and the accuracy reaches 93.2%. The results of the comparative analysis show that the proposed feature of attribute approach is simpler, more effective and easier to use than the existing well-formed structural partial ordered attribute diagram approach.