Analyzing Subjecthood Tests in Korean Using a Cross-Validation
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초록
Many diagnostics have been proposed to examine subjecthood in Korean, some of which were investigated in terms of their validity as subject diagnostics through a series of experimental studies. This paper takes the experimental data from three experimental studies (Kim et al., 2015; Lee et al., 2015a; Kim et al., 2017) that were previously conducted and examines the validity of six diagnostic tests using a cross-validation and a machine learning method. The experimental data were evenly divided into 10 parts (10-fold cross-validation): Nine of them were used for machine learning (training data set) and the remaining one was for the testing (test data set). The analysis was conducted 10 times, and the average values were taken. A simple linear regression was conducted for machine learning. Through the analysis, the following results were observed: (i) The accuracy of the diagnostics for SSC was much higher than that of MSC (SSC: 81,78, MSC: 68.66), (ii) Adjunct Control was the most accurate for SSC, whereas Coordinated Deletion for MSC, (iii) HA and PL had low performance. The results also seem to imply that there are some parts which cannot be explained by a simple linear model but can be analyzed with non-linear modeling of language.
Abstract
Many diagnostics have been proposed to examine subjecthood in Korean, some of which were investigated in terms of their validity as subject diagnostics through a series of experimental studies. This paper takes the experimental data from three experimental studies (Kim et al., 2015; Lee et al., 2015a; Kim et al., 2017) that were previously conducted and examines the validity of six diagnostic tests using a cross-validation and a machine learning method. The experimental data were evenly divided into 10 parts (10-fold cross-validation): Nine of them were used for machine learning (training data set) and the remaining one was for the testing (test data set). The analysis was conducted 10 times, and the average values were taken. A simple linear regression was conducted for machine learning. Through the analysis, the following results were observed: (i) The accuracy of the diagnostics for SSC was much higher than that of MSC (SSC: 81,78, MSC: 68.66), (ii) Adjunct Control was the most accurate for SSC, whereas Coordinated Deletion for MSC, (iii) HA and PL had low performance. The results also seem to imply that there are some parts which cannot be explained by a simple linear model but can be analyzed with non-linear modeling of language.
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