• Korean Journal of Human Ecology
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• v.14 no.6
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• pp.961-972
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• 2005

The purpose of this study is to investigate the improvement of English proficiency of children in the ESL and EFL learning style classrooms through the experiment method. The results of this research are as follows: first, the scores of listening and speaking and the perception of alphabets in the ESL program are higher than that in the EFL program. This means that learning in the ESL style classroom is the better way to improve English skills than in the EFL style classroom, which is common in Korea. Second, there is no difference in the English listening and speaking skills and the perception of the English alphabets between the two gender groups in the ESL & EFL style classrooms. These results suggest that the target language may be used in the English classrooms by the teachers and the students with the materials, books, and equipment are English. Teachers are expected to be in charge of playing decisive roles as demonstrators of speech, models and correctors of pronunciation and providers of materials including TV, VCR, CD players, and cassette recorders, etc.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.159-164
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• 2005

In a computing environment connected pan-based computers and a server by Internet, This paper describes a language learning system evaluating the quality of a handwriting string. For the purpose of the system, this paper explains how to retrieve reference data from a database, how to evaluate the quality of a handwriting string using global and local features. The Proposed system can evaluate the qualify of a handwriting string as well as a handwriting character. The qualify can be computed in the case of different language between reference and input. Therefore, we expect that the system is very useful not only for training on handwriting but also learning a language.

• Health Policy and Management
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• v.30 no.1
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• pp.15-25
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• 2020

While healthcare data sets include extensive information about patients, many researchers have limitations in analyzing them due to their intrinsic characteristics such as heterogeneity, longitudinal irregularity, and noise. In particular, since the majority of medical history information is recorded in text codes, the use of such information has been limited due to the high dimensionality of explanatory variables. To address this problem, recent studies applied word embedding techniques, originally developed for natural language processing, and derived positive results in terms of dimensional reduction and accuracy of the prediction model. This paper reviews the deep learning-based natural language processing techniques (word embedding) and summarizes research cases that have used those techniques in the health care field. Then we finally propose a research framework for applying deep learning-based natural language process in the analysis of domestic health insurance data.

• Cross-Cultural Studies
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• v.37
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• pp.267-286
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• 2014

The purpose of this study is to improve the writing skills through activities to read and summarize the internet children newspaper article. The subjects of study are the college students of A2-B1 level in the French writing classes. The range of study was as follows: - As the previous activity of writing, activities of teaching and learning of vocabularies to comprehend the internet children newspaper article. - learn about the rules of summary - writing the summary The children's newspaper used in this study has the advantage that can increase the learning motivation of learners as having a topicality by itself and a level of easy language. The summary activities can be called a comprehensive activities of teaching and learning that combine the critical reading ability that can distinguish important information and secondary one with the creative writing ablility that can reconstruct one's own style from the selected content. In addition, the summary assists the understanding of a text and is a help to its memory. It is the strategy of reading comprehension and also is simultaneously the strategy of writing that can write with one's own vocabulary by newly structuring the text. The results of this study will provide a vitality for the education environment and field of study of French language that have been neglected the writing ability. Moreover it will be the motivation to propose a way of a balanced French language communication to our French language learners weighted on oral communication.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.369-376
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• 2021

One of the most active areas of research in the field of affective computing and signal processing is emotion recognition. This paper proposes emotion recognition of low-resource (Sindhi) language. This work's uniqueness is that it examines the emotions of languages for which there is currently no publicly accessible dataset. The proposed effort has provided a dataset named MAVDESS (Mehran Audio-Visual Dataset Mehran Audio-Visual Database of Emotional Speech in Sindhi) for the academic community of a significant Sindhi language that is mainly spoken in Pakistan; however, no generic data for such languages is accessible in machine learning except few. Furthermore, the analysis of various emotions of Sindhi language in MAVDESS has been carried out to annotate the emotions using line features such as pitch, volume, and base, as well as toolkits such as OpenSmile, Scikit-Learn, and some important classification schemes such as LR, SVC, DT, and KNN, which will be further classified and computed to the machine via Python language for training a machine. Meanwhile, the dataset can be accessed in future via https://doi.org/10.5281/zenodo.5213073.

• Smart Media Journal
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• v.12 no.1
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• pp.32-46
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• 2023

Natural Language Processing (NLP) has grown tremendously in recent years. Typically, bilingual, and multilingual translation models have been deployed widely in machine translation and gained vast attention from the research community. On the contrary, few studies have focused on translating between spoken and sign languages, especially non-English languages. Prior works on Sign Language Translation (SLT) have shown that a mid-level sign gloss representation enhances translation performance. Therefore, this study presents a new large-scale Korean sign language dataset, the Museum-Commentary Korean Sign Gloss (MCKSG) dataset, including 3828 pairs of Korean sentences and their corresponding sign glosses used in Museum-Commentary contexts. In addition, we propose a translation framework based on self-supervised learning, where the pretext task is a text-to-text from a Korean sentence to its back-translation versions, then the pre-trained network will be fine-tuned on the MCKSG dataset. Using self-supervised learning help to overcome the drawback of a shortage of sign language data. Through experimental results, our proposed model outperforms a baseline BERT model by 6.22%.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.141-150
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• 2021

Based on Artificial Intelligence technology, AI-enabled warfare is expected to become the main issue in the future warfare. Natural language processing technology is a core technology of AI technology, and it can significantly contribute to reducing the information burden of underrstanidng reports, information objects and intelligences written in natural language by commanders and staff. In this paper, we propose a Language model-based Multi-source Information Integration (LAMII) framework to reduce the information overload of commanders and support rapid decision-making. The proposed LAMII framework consists of the key steps of representation learning based on language models in self-supervsied way and document integration using autoencoders. In the first step, representation learning that can identify the similar relationship between two heterogeneous sentences is performed using the self-supervised learning technique. In the second step, using the learned model, documents that implies similar contents or topics from multiple sources are found and integrated. At this time, the autoencoder is used to measure the information redundancy of the sentences in order to remove the duplicate sentences. In order to prove the superiority of this paper, we conducted comparison experiments using the language models and the benchmark sets used to evaluate their performance. As a result of the experiment, it was demonstrated that the proposed LAMII framework can effectively predict the similar relationship between heterogeneous sentence compared to other language models.

• Proceedings of the KSPS conference
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• 1996.10a
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• pp.274-274
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• 1996

This presents the preliminary results from work in progress of a paired study of the acquisition of voiceless stops by Spanish speakers learning English, and American English speakers learning Spanish. For this study the hypothesis was that the American speakers would have no difficulty suppressing the aspiration in Spanish unaspirated stops; the Spanish speakers would have difficulty acquiring the aspiration necessary for English voiceless stops, according to Eckman's Markedness Differential Hypothesis. The null hypothesis was proved. All subjects were given the same set of disyllabic real words of English and Spanish in carrier phrases. The tokens analyzed in this report are limited to word-initial voiceless stops, followed by a low back vowel in stressed syllables. Tokens were randomized and then arranged in a list with the words appearing three separate times. Aspiration was measured from the burst to the onset of voicing(VOT). Both the first language (Ll) tokens and second language (L2) tokens were compared for each speaker and between the two groups of language speakers. Results indicate that the Spanish speakers, as a group, were able to reach the accepted target language VOT of English, but English speakers were not able to reach the accepted range for Spanish, in spite of statistically significant changes of p<.OOl by speakers in both groups of learners. A closer analysis of the speech samples revealed wide variability within the speech of native speakers of English. Not only is variability in English due to the wide range of VOT (120 msecs. for English labials, for example) but individual speakers showed different patterns. These results are revealing for the demands requied in experimental designs and the number of speakers and tokens requied for an adequate description of different languages. In addition, a simple report of means will not distinguish the speakers and the respective language learning situation; measurements must also include the RANGE of acceptability of VOT for phonetic segments. This has immediate consequences for the learning and teaching of foreign languages involving aspirated stops. In addition, the labelling of spoken language in speech technology is shown to be inadequate without a fuller mathematical description.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.124-130
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• 2017

Character encoding is the method used to represent characters or symbols on a computer, and there are many encoding detection software tools. For the widely used encoding detection software"uchardet", the accuracy of encoding detection of unmodified normal text document is 91.39%, but the accuracy of language detection is only 32.09%. Also, if a text document is encrypted by substitution, the accuracy of encoding detection is 3.55% and the accuracy of language detection is 0.06%. Therefore, in this paper, we propose encoding and language detection of text document using the deep learning algorithm called LSTM(Long Short-Term Memory). The results of LSTM are better than encoding detection software"uchardet". The accuracy of encoding detection of normal text document using the LSTM is 99.89% and the accuracy of language detection is 99.92%. Also, if a text document is encrypted by substitution, the accuracy of encoding detection is 99.26%, the accuracy of language detection is 99.77%.

• English Language & Literature Teaching
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• v.15 no.3
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• pp.129-150
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• 2009

This paper attempts to examine the effect of children's English stories on the development of Korean EFL primary school learners' listening and speaking competences and their motivation to learn English. This paper also discusses factors of English children's stories that make EFL learners' language learning efficient. Participants were 120 primary school students who attend one of the elementary schools in Chungnam province. They were randomly chosen and divided into two groups: experimental and control groups. In order to collect data, students' listening and speaking proficiency pre- and post-tests and the pre- and post-questionnaires regarding the participants' motivation to learn English were administered. The data were analyzed by ANOVA. The results indicate that the application of English children's stories to EFL learning settings can be an efficient way to improve EFL learners' listening and speaking competences and motivation to learn their target language. The findings of this study suggest that English children's stories provide language learners with interest, meaningful and authentic contexts and enjoyment. The pedagogical suggestion and implications are provided for EFL educators and teachers.