Statistical models for case ambiguity resolution in Korean
This thesis deals with the resolution of case ambiguity in Korean. Even though Korean is a case marked language, in which phonetically recognisable case markers (<i>case particles </i>in Korean) mark cases explicitly, nominal words without any accompanying case particles are used frequently in naturally occurring texts and speech. When the case particles are not present, it is basically a mater of conjecture to infer the grammatical function of the nominal words. The position of a nominal word itself cannot give much help as Korean is a relatively free word order language. The case ambiguity problem has brought a great controversy in Korean linguistics and has been regarded as an unavoidable obstacle for automatic processing of the Korean language. To tackle the case ambiguity problem, we adopt knowledge-lean statistical methods. Our challenge is to construct effective statistical methods for case ambiguity resolution in Korean without using fully annotated training material that does not exist currently. In other words, we attempt to train our statistical models on the training data collected from non-ambiguous case marking instances in a corpus. As a first step, after briefly surveying the concept of <i>case</i>
Lee, Kihwang
Statistical models for case ambiguity resolution in Korean. This thesis deals with the resolution of case ambiguity in Korean. Even though Korean is a case marked language, in which phonetically recognisable case markers (<i>case particles </i>in Korean) mark cases explicitly, nominal words without any accompanying case particles are used frequently in naturally occurring texts and speech. When the case particles are not present, it is basically a mater of conjecture to infer the grammatical function of the nominal words. The position of a nominal word itself cannot give much help as Korean is a relatively free word order language. The case ambiguity problem has brought a great controversy in Korean linguistics and has been regarded as an unavoidable obstacle for automatic processing of the Korean language. To tackle the case ambiguity problem, we adopt knowledge-lean statistical methods. Our challenge is to construct effective statistical methods for case ambiguity resolution in Korean without using fully annotated training material that does not exist currently. In other words, we attempt to train our statistical models on the training data collected from non-ambiguous case marking instances in a corpus. As a first step, after briefly surveying the concept of <i>case</i>
statistical_model case ambiguity resolution korean deal resolution case ambiguity korean even_though korean case mark language phonetically recognisable case marker case korean mark case explicitly nominal word without accompany case particle use frequently naturally_occur texts speech case present basically mat infer grammatical_function nominal word position nominal word give much help korean relatively free_word order language case ambiguity problem bring_great controversy korean linguistics regard obstacle automatic processing korean language tackle case ambiguity problem adopt knowledge lean statistical_method challenge construct effective statistical_method case ambiguity resolution korean without use fully annotate train material exist currently word attempt train statistical_model train data_collect non ambiguous case mark instance corpus first_step briefly survey concept case