Abstract
AbstractThis study aims to improve the performance of identifying grammatical relations between a noun phrase and a verb phrase in Korean sentences. The key task is to determine the relation between the two constituents in terms of such grammatical relational categories as subject, object, complement, and adverbial. To tackle this problem, we propose to employ the Support Vector Machines (SVM) in determining the grammatical relations. Through an experiment with a tagged corpus for training SVMs, we found the proposed model to be more useful than both the Maximum Entropy model and the backed-off method.KeywordsSupport Vector MachineNoun PhraseHead NounMaximum Entropy ModelGrammatical FunctionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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