• Journal of Information Processing Systems
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• v.18 no.4
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• pp.535-548
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• 2022

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

• Asia pacific journal of information systems
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• v.33 no.4
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• pp.1118-1134
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• 2023

Previous research has shown that employees tend to react more positively to corrective feedback from supervisors to the extent they perceive that they were treated with empathy, respect, and concern towards fair interpersonal treatment in receiving the feedback information. Then, to facilitate effective supervisory feedback and coaching, it would be useful for organizations to monitor the contents of feedback exchanges between supervisors and employees to make sure that supervisors are providing performance feedback using languages that are more likely to be perceived as interpersonally fair. Computer-aided text analysis holds potential as a useful tool that organizations can use to efficiently monitor the quality of the feedback messages that supervisors provide to their employees. In the current study, we applied computer-aided text analysis (using closed-vocabulary text analysis) and machine learning to examine the validity of language-based algorithms trained on supervisor language in performance feedback situations for predicting human ratings of feedback interpersonal fairness. Results showed that language-based algorithms predicted feedback interpersonal fairness with reasonable level of accuracy. Our findings provide supportive evidence for the promise of using employee language data for managing (and improving) performance management in organizations.

• Journal of Korea Entertainment Industry Association
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• v.13 no.6
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• pp.265-275
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• 2019

The purpose of this study was to analysis of the changes of the white noise on the change of the writing ability and hand function of the children with the intellectual disabilities and then provide the basic information about that. The subjects was 12 children with intellectual disabilities. White noise was applied to analyze the subjects' writing ability and hand function before and after application. The provision of the white noise was continuous and uniform through the white noise generator. The analysis of the writing ability was performed by using the KNISE-BAAT assessment and the writing, vocabulary and composing ability were evaluated for the writing ability of the subjects. Also, the analysis of the hand function was performed by using the pegboard sub-item of the Manual Function Test. The results of the writing ability showed the statistically significant increase of the writing and vocabulary ability, but in the case of the composing ability, there was no statistically significant increase in the composing ability. Also, the results of the hand function showed the statistically significant increase in the both hands. The use of the white noise should be considered as a compensatory approach to improve the writing ability and hand function of the children with intellectual disabilities. Also, in order to improve the level of the performance, learning level, and academic achievement of the children of the intellectual disabilities, the application of the white noise in the living and learning environment should be needed to consider.

• Smart Media Journal
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• v.12 no.11
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• pp.185-197
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• 2023

This study was conducted using text mining and network theory to extract useful information for application for occupancy and performance of permit tasks contained in the permit contents from the permit register, which is used only for the simple purpose of recording occupancy permit information. Based on text mining, we analyzed and compared the frequency of vocabulary occurrence and topic modeling in five regions, including Seoul, Gyeonggi, Gyeongsang, Jeolla, Chungcheong, and Gangwon, as well as normalization processes such as stopword removal and morpheme analysis. By applying four types of centrality algorithms, including stage, proximity, mediation, and eigenvector, which are widely used in network theory, we looked at keywords that are in a central position or act as an intermediary in the network. Through a comprehensive analysis of vocabulary appearance frequency, topic modeling, and network centrality, it was found that the 'installation' keyword was the most influential in all regions. This is believed to be the result of the Ministry of Environment's permit management office issuing many permits for constructing facilities or installing structures. In addition, it was found that keywords related to road facilities, flood control facilities, underground facilities, power/communication facilities, sports/park facilities, etc. were at a central position or played a role as an intermediary in topic modeling and networks. Most of the keywords appeared to have a Zipf's law statistical distribution with low frequency of occurrence and low distribution ratio.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.173-180
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• 2024

We This study aimed to quantitatively analyze the theme of 'libertinage' and the associated issues of reason and emotion in 『Dangerous Liaisons』, a novel considered a masterpiece of libertine literature and an epistolary novel of the 18th century, using digital analysis tools. First, based on the frequency analysis of word usage using Voyant and LIWC 22, we confirmed that libertinage is manifested with keywords such as 'love' and 'time'. With Voyant's 'Contexts' feature, it was found that the letters sent by Valmont to Madame de Tourvel and those sent by Madame de Merteuil both have 'love' as the central theme. However, emotional vocabulary was higher in the former, whereas strategic vocabulary was more prevalent in the latter. Additionally, it was observed that the most frequently used word in the letters sent by Madame de Merteuil is 'time', with a higher frequency than 'love'. Thirdly, using LIWC 22, we measured the analytical thinking and emotional tone of the letters exchanged by the main characters, and analyzed how these values changed according to the chapters. Through these analyses, we confirmed that this novel, alongside Rousseau's "New Eloise," anticipates romanticism by embracing the theme of 'emotion,' which was rejected by 18th-century Enlightenment ideals.

• MALSORI
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• no.43
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• pp.151-161
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• 2002

This study presents performance analysis results of speaker adaptation for keyword spotting system. In this paper, we implemented MLLR (Maximum Likelihood Linear Regression) method on our middle size vocabulary keyword spotting system. This system was developed for directory services of universities and colleges. The experimental results show that speaker adaptation reduces the false alarm rate to 1/3 with the preservation of the mis-detection ratio. This improvement is achieved when speaker adaptation is applied to not only keyword models but also non-keyword models.

• MALSORI
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• no.52
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• pp.111-120
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• 2004

This paper focuses on the DSP implementation of an HMM-based speech recognizer that can handle several hundred words of vocabulary size as well as speaker independency. First, we develop an HMM-based speech recognition system on the PC that operates on the frame basis with parallel processing of feature extraction and Viterbi decoding to make the processing delay as small as possible. Many techniques such as linear discriminant analysis, state-based Gaussian selection, and phonetic tied mixture model are employed for reduction of computational burden and memory size. The system is then properly optimized and compiled on the TMS320C6711 DSP for real-time operation. The implemented system uses 486kbytes of memory for data and acoustic models, and 24.5 kbytes for program code. Maximum required time of 29.2 ms for processing a frame of 32 ms of speech validates real-time operation of the implemented system.

• English Language & Literature Teaching
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• v.17 no.3
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• pp.255-272
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• 2011

As the demand of English education is increasing, the demand for Native English speaking teachers (NEST) is rising, especially in Asian countries. However, due to the low number of NEST, the Korean government is suggesting that Filipino Women be used as English teachers as an alternative. This study aims to answer three questions: (1) are Filipino women in Korea qualified to teach English based on the error analysis of their written essays? (2) what are the linguistic features found in their diagnostic essays? and (3) is their written English better than Korean college students' written English based on the comparison of the two groups? The findings from the Filipino participants show the most frequently occurring errors are related to punctuation usage (commas and hyphens), vocabulary (word choice), verb usage, redundancy, and even as basic as capitalization usage. The results from the comparison of the two groups show that the percentage of the Filipino participants' written error was 14% while the percentage of the Korean participants was 17%. The findings would give us some ideas on the qualification of Filipino women in Korea as English teachers.

• English Language & Literature Teaching
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• v.12 no.3
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• pp.1-24
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• 2006

This study investigated differences in beliefs about English learning of 286 EFL college students and 52 English teachers in Korea. Data was collected using Horwitz's Beliefs About Language Learning Inventory and compared between students and teachers in beliefs. To address the research questions, the data were analyzed through descriptive statistics including frequencies, factor analysis, MANOVA, ANOVA, t-test, and reliability coefficients. The results showed four factors in student beliefs: Difficulty of learning English, nature of learning English, importance of correctness in learning English, and motivation and perceived importance of learning English. Clear differences were found in students and teachers' beliefs in English learning aptitude and importance of translation, error correction, and grammar rules. A few belief differences were also identified between Koreans and native-speaking English teachers related to the importance of vocabulary learning, pronunciation, and cultural knowledge. The findings of the study indicated that background variables such as gender and major field of study have an effect on student beliefs about L2 learning. The present study also provided pedagogical considerations to reduce mismatch between students and teachers beliefs and to improve the L2 planning and instruction.

• Korean Journal of Child Studies
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• v.25 no.2
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• pp.41-57
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• 2004

Subjects were 88 two-year-old-toddlers(25-36 months of age) and 32 teachers in 13 childcare centers; they were randomly assigned to experimental or control groups. The researcher observed teacher-toddler interaction in the picturebook reading situation. Analysis of patterns of teachers' verbal behavior and coding of toddlers' verbal and nonverbal behaviors were based on Senechal, et al.(1995) and Whitehurst, et al.(1988), respectively. In comparison with the control group, toddlers of the experimental group showed significant differences in verbal behavior; they acquired nouns occurring in the picturebooks and more expressive and comprehensive language. After training intervention, teachers of the experimental group showed changes in quality and quantity of verbal behavior.