This paper proposed the optimal attributes for text classification based on Korean and Chinese linguistic features. The experiments committed to discover which is the best feature among n-grams which is known as language independent, morphemes that have language dependency and some other feature sets consisted with n-grams and morphemes showed best results. This paper used SVM classifier and Internet news for text classification. As a result, bi-gram was the best feature in Korean text categorization with the highest F1-Measure of 87.07%, and for Chinese document classification, 'uni-gram+noun+verb+adjective+idiom', which is the combined feature set, showed the best performance with the highest F1-Measure of 82.79%.

Abstract. In this paper, we present a multi-pass coreference resolution model using context restriction. Coreference resolution is the task of finding all expressions such as words or phrases that refer to the same entity in a text. First, we classified coreference types into seven according to the accuracy of the types, and then modelled a sequential rule-based approach that restricts global or local context ranges at each step. Since anaphors generally refer to the nearest preceding individuals with same attributes, position information is one of the important features in coreference resolution.

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