• Journal of the Korean Society of Earth Science Education
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• v.5 no.2
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• pp.158-165
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• 2012

With five gifted and nine average elementary school students, this study attempted to make a comparative analysis on the characteristics of their scientific writings for earth science-related topics. The analysis found that all of the gifted students showed higher scores than the average in the writing sections of scientific nature, logical nature and creativity. Compared to the average scores, their creativity scores were far higher. By comparing and analyzing the predicates in the writings two groups wrote, I found that the gifted students used more sentences per topic than the average students. Both groups wrote the most numbers of sentences for Volcano-related topics. In the meantime, the gifted children used the least numbers of sentences for the related topics to atmospheric pollution and the average students did so for the related topics to fossils. By the analysis on the patterns of predicate, it was observed that both groups used material predicates most and verbal predicates least. As far as the second most used predicates are concerned, the gifted children used relational predicates and the average students used mental predicates.

• Korean Journal of Cognitive Science
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• v.1 no.2
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• pp.255-257
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• 1989

This paper proposes a method for extracting the imformation of aspect and modality from the predicative part which is consisted of main verbal and auxiiary verbals.Data which are expressed by the compound predicate with many consecutive verbals are collected and analyzed to thirty-six structual forms of the predicative part.Inthe final analysis, an extracting function of conceptual information is derived to find the connoted aspect and modality in each structure.The informations which are obtained by this function decrease the individual ambiguity of an auxiliary verbal and offer a detailed meaning inthe syntactic and semantic analysis of machine translation system or inference machine.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5541-5554
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• 2018

A voice is one of the most significant non-verbal elements for communication. Disorders in vocal organs, or habitual muscular setting for articulatory cause vocal disorders. Therefore, by analyzing the vocal disorders, it is possible to predicate vocal diseases. In this paper, a method of predicting vocal disorders using the jitter, shimmer, and harmonics-to-noise ratio (HNR) extracted from vocal records is proposed. In order to extract jitter, shimmer, and HNR, one-second's voice signals are recorded in 44.1khz. In an experiment, 151 voice records are collected. The collected data set is clustered using cobweb clustering method. 21 classes with 12 leaves are resulted from the data set. According to the semantics of jitter, shimmer, and HNR, the class whose centroid has lowest jitter and shimmer, and highest HNR becomes the normal vocal group. The risk of vocal disorders can be predicted by measuring the distance and direction between the centroids.

• International Journal of Advanced Culture Technology
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• v.7 no.2
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• pp.168-178
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• 2019

In this paper I discuss a more complicated case of 'It'-Extraposition in English in the Optimality Theory [1] by further modifying and extending the analysis done in Khym (2018) [2] in which only the 'relatively' simple cases of 'It'-Extraposition such as 'CP-Predicate' was dealt with. I show in this paper that the constraints and the constraint hierarchy developed to explain the 'relatively' simple cases of 'It'-Extraposition are no longer valid for the more complicated cases of 'It'-Extraposition in configuration of 'CP-V-CP'. In doing so, I also discuss two important theoretic possibilities and suggest a new view to look at the 'It'-Extraposition: first, the long-bothering question of which syntactic approach between P&P (Chomsky 1985) [3] and MP (Chomsky 1992) [4] should be based on in projecting the full surface forms of candidates may boil down to just a simple issue of an intrinsic property of the Gen(erator). Second, the so-called 'It'- Extraposition phenomenon may not actually be a derived construction by the optional application of Extraposition operation. Rather, it could be just a representational construction produced by the simple application of 'It'-insertion after the structure projection with 'that-clause' at the post-verbal position. This observation may lead to elimination of one of the promising candidates of '$It_i{\ldots}[_{CP}that{\sim}]_i$' out of the computation table in Khym [2], and eventually to excluding the long-named 'It'-Extraposition case from Extrsposition phenomena itself. The final constraints and the constraint hierarchy that are explored are as follows: ${\bullet}$ Constraints: $^*SSF$, AHSubj, Subj., Min-D ${\bullet}$ Constraint Hierarchy: SSF<<>>Subj.>> AHSubj.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.555-562
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• 2016

Semantic Role Labeling (SRL) is to determine the semantic relation of a predicate and its argu-ments in a sentence. But Korean semantic role labeling has faced on difficulty due to its different language structure compared to English, which makes it very hard to use appropriate approaches developed so far. That means that methods proposed so far could not show a satisfied perfor-mance, compared to English and Chinese. To complement these problems, we focus on suffix information analysis, such as josa (case suffix) and eomi (verbal ending) analysis. Korean lan-guage is one of the agglutinative languages, such as Japanese, which have well defined suffix structure in their words. The agglutinative languages could have free word order due to its de-veloped suffix structure. Also arguments with a single morpheme are then labeled with statistics. In addition, machine learning algorithms such as Support Vector Machine (SVM) and Condi-tional Random Fields (CRF) are used to model SRL problem on arguments that are not labeled at the suffix analysis phase. The proposed method is intended to reduce the range of argument instances to which machine learning approaches should be applied, resulting in uncertain and inaccurate role labeling. In experiments, we use 15,224 arguments and we are able to obtain approximately 83.24% f1-score, increased about 4.85% points compared to the state-of-the-art Korean SRL research.