• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.1-10
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• 2022

The purpose of this study was to investigate how the characteristics of mobile-based financial platforms have an impact on the intention to use loan service users. In addition, it was attempted to investigate whether usefulness and ease of use had a mediating effect in the relationship between each characteristic of the mobile financial platform on the intention to use the loan service. Data collection was conducted from March 1 to April 30, 2022, and 200 people participated in the study. Analysis methods were frequency analysis, exploratory factor analysis, reliability analysis, correlation analysis, hierarchical multiple regression analysis, and three-step mediation regression analysis. The research results are as follows. First, the influence of user factors, technical factors, and environmental factors of a financial platform on the intention to use a mobile loan service was found to be ubiquity in user factors, reliability in technical factors, and facilitation conditions in environmental factors. Second, in the relationship between convenience and intention to use user factors, usefulness had a completely mediating effect. Third, in the relationship between reliability of technical factors and intention to use, usefulness showed a partial mediating effect. Fourth, in the relationship between the social impact of environmental factors and facilitation conditions and intention to use, the usefulness showed a partial mediating effect. Fifth, ease of use showed a completely mediating effect in the relationship between convenience and intention of use of user factors. Sixth, in the relationship between reliability of technical factors and intention to use, ease of use showed a partial mediating effect. Seventh, in the relationship between the social impact of environmental factors and intention to use, ease of use showed a partial mediating effect, and in the relationship between facilitation conditions and intention to use, ease of use showed a fully mediating effect. Through this study, we tried to present basic data on the determinants of the user's acceptable intention to use the mobile loan service.

• Composites Research
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• v.35 no.4
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• pp.248-254
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• 2022

In this study, TiB₂-steel composite with high-fractional TiB₂ reinforcement was fabricated by gas pressure infiltration process and the microstructure analysis and compressive strength and hardness were evaluated. To elucidate the correlation between microstructure and mechanical properties for fabricated composite, after the compression test of TiB₂-steel composite, the fracture surface was analyzed and the fracture behavior on compression test was predicted. As a result of the compression fracture surface analysis, interfacial failure trace between the steel matrix and the reinforcement was observed, and the interface between the steel matrix and the reinforcement was analyzed using TEM. From the result of microstructure analysis on the fabricated composite, it was confirmed that, in addition to TiB₂ reinforcement and steel matrix, TiC phase and coarse (Fe,M)₂B (M=Cr,Mn) phase were formed. Throughout the thermodynamic calculation, it was confirmed that TiC and (Fe,M)₂B can be formed as a stable phase under the process condition. The fabricated TiB₂-steel composite had a significantly increased hardness, and the compressive strength and Young's modulus were improved by 3.07 times and 1.95 times, respectively, compared to steel matrix. It seems that the coarse (Fe,M)₂B (M=Cr,Mn) phase formed throughout the composite causes the deterioration of mechanical properties, and by controlling the formation of the (Fe,M)₂B (M=Cr,Mn) phase, it is judged that the mechanical properties of the TiB₂-steel composite can be further improved.

• The Korean Journal of Coaching Psychology
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• v.5 no.2
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• pp.127-170
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• 2021

This study developed a measure to measure wellness-seeking behavior in important areas of life for general adults in Korea and verified its validity. For the development of the wellness scale, 31 factors derived through literature review, expert interviews, in-depth interviews, open questionnaires 1 and 2, and 182 questions were selected as the final 10 factors and 99 questions. Through exploratory factor analysis of the results of the preliminary survey of 351 adults in Korea, 58 questions of 10 factors were derived, and some of the questions reflecting important concepts in each factor were revised, and this survey was conducted with 63 questions of 10 factors. In this survey conducted on 667 people, to verify the validity of the composition concept of this test, the entire sampling was divided into two groups, one group was subjected to exploratory factor analysis, and the other group was subjected to confirmatory factor analysis. As a result of exploratory factor analysis, 63 questions of 10 factors (work, community, family, others, economic power, self-esteem, leisure, physical health, spirituality, and self-growth) were finally derived, and confirmatory factor analysis using the structural equation model verified that the model fit criteria were met. Convergence validity was verified using the K-MHC-SF and Wellness Index for Workers to verify whether the derived wellness scale and its sub-factors actually measure wellness. As a result of analyzing the relationship between the variables and factors of the Subjective Happiness Scale and Life Scale to verify the validity related to the criteria, it was found to be a significant correlation. As a result of confirming the significance of each path through multiple regression analysis, the 'self-esteem' on the wellness scale was identified as the most important factor influencing subjective happiness and life satisfaction. Finally, discussions on this research process and results, academic significance and practical significance, limitations, and future research directions were presented.

• Therapeutic Science for Rehabilitation
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• v.12 no.4
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• pp.81-96
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• 2023

Objective : This study aimed to examine the association between the structural and functional aspects of social networks and health-promoting behaviors in community-dwelling older adults. Methods : Social networks based on structural and functional aspects and health-promoting behaviors by lifestyle were measured in 226 community-dwelling adults aged 65 years and over. To analyze the collected data, an independent t-test, one-way ANOVA, Pearson's correlation analysis, and hierarchical regression were performed. Results : The network size was the highest in the friend network, and the frequency of contact and social support was the highest in the non-living family network. Health-promoting behaviors were highest for activities of daily living and lowest for productive and social activities. All subfactors of social networks showed significant positive correlations with health-promoting behaviors. Hierarchical regression showed social support from neighbors as the variable with a significant effect on health-promoting behaviors; gender and depression were also influencing factors, and this model showed 37% explanatory power. Conclusion : To promote healthy behaviors of older adultsin the community, the development of health promotion programs and related policies considering social networks centering on social support from neighbors is required.