• 한국수학교육학회지시리즈D:수학교육연구
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• 제17권3호
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• pp.169-180
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• 2013

Communicating about mathematics is an essential component in learning mathematics and is a key standard for successful learning in a mathematics classroom using stories and storytelling as a catalyst to mathematics instruction. This, however, can make learning math for students with language deficiencies since they are working toward mastering both basic language proficiency as well as the specialized language needed for mathematics. This is a particular concern because the number of students of multicultural families is rapidly increasing. In this paper, we discuss the challenges and complexities of language-deficient students learning math in a classroom where communication is a key standard for successful learning, and suggest implications for teaching, by presenting an USA elementrny teacher's scaffolding to make reading and solving word problems less intimidating for her language learner students as well as native speaking students.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1307-1312
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• 2004

Word sense disambiguation is one of the most important problems in natural language processing research topics such as information retrieval and machine translation. Many approaches can be employed to resolve word ambiguity with a reasonable degree of accuracy. These strategies are: knowledge-based, corpus-based, and hybrid-based. This paper pays attention to the corpus-based strategy. The purpose of this paper is to compare three famous machine learning techniques, Snow, SVM and Naive Bayes in Word-Sense Disambiguation on Thai language. 10 ambiguous words are selected to test with word and POS features. The results show that SVM algorithm gives the best results in solving of Thai WSD and the accuracy rate is approximately 83-96%.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2003년도 추계공동학술대회
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• pp.721-727
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• 2003

This study designed and implemented a system to extract of vowel from a word. The system is comprised of a voice feature extraction module and a neutral network module. The voice feature extraction module use a LPC(Linear Prediction Coefficient) model to extract a voice feature from a word. The neutral network module is comprised of a learning module and voice recognition module. The learning module sets up a learning pattern and builds up a neutral network to learn. Using the information of a learned neutral network, a voice recognition module extracts a vowel from a word. A neutral network was made to learn selected vowels(a, eo, o, e, i) to test the performance of a implemented vowel extraction recognition machine. Through this experiment, could confirm that speech recognition module extract of vowel from 4 words.

• 한국멀티미디어학회논문지
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• 제24권10호
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• pp.1391-1402
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• 2021

This paper focuses use context-sensitive spelling error correction using generative adversarial network. Generative adversarial network[1] are attracting attention as they solve data generation problems that have been a challenge in the field of deep learning. In this paper, sentences are generated using word embedding information and reflected in word distribution representation. We experiment with DCGAN[2] used for the stability of learning in the existing image processing and D2GAN[3] with double discriminator. In this paper, we experimented with how the composition of generative adversarial networks and the change of learning corpus influence the context-sensitive spelling error correction In the experiment, we correction the generated word embedding information and compare the performance with the actual word embedding information.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2009년도 춘계학술대회 미래 IT융합기술 및 전략
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• pp.208-211
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• 2009

본 논문은 구글(Google), 워드넷(WordNet)과 같이 공개된 웹 자원과 리소스를 이용한 비교사학습(Unsupervised learning) 방법을 제안하여 단어 의미의 중의성 문제를 해결하고자 한다. 구글 검색 API를 이용하여 단어의 확장된 근접 문맥정보를 추출하고, 워드넷의 계층체계와 synset을 이용하여 단어 의미 구분정보를 자동 추출한 후, 추출된 정보 간 유사도 계산을 통해 중의성을 갖는 단어의 의미를 결정한다.

• 영어어문교육
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• 제13권4호
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• pp.167-192
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• 2007

This study investigates the effects of written vocabulary exercises on lexical knowledge. Korean university students learning English as a foreign language were randomly assigned to one of four conditions-Condition 1 (having students match word form with word meaning), Condition 2 (having students fill in the blank provided with a list of words), Condition 3 (having students write sentences with the target words), Condition 4 (having students do three practices with the same vocabulary exercise as the condition 1). Each type of exercises in Condition 1, 2, and 3 was designed to classify a different level of mental processing except Condition 4 with multiple encounters of the target words. Learners' vocabulary knowledge of this study was obtained using a format adopted from the Vocabulary Knowledge Scale (VKS) immediately and two weeks later. The findings indicated that: (1) Condition 4 having students do three matching vocabulary exercises was as effective as the condition 3 (one writing exercise) on the immediate learning of word; (2) although there was no significant difference of the effect of vocabulary exercises between Condition 3 and 4, Condition 4 asking students to do three matching vocabulary exercises was the most effective way of vocabulary retention after two weeks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.3169-3181
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• 2015

Nowadays, online word-of-mouth has become a powerful influencer to marketing and sales in business. Opinion mining and sentiment analysis is frequently adopted at market research and business analytics field for analyzing word-of-mouth content. However, there still remain several challengeable areas for 1) sentiment analysis aiming for Korean word-of-mouth content in film market, 2) availability of machine learning models only using linguistic features, 3) effect of the size of the feature set. This study took a sample of 10,000 movie reviews which had posted extremely negative/positive rating in a movie portal site, and conducted sentiment analysis with four machine learning algorithms: naïve Bayesian, decision tree, neural network, and support vector machines. We found neural network and support vector machine produced better accuracy than naïve Bayesian and decision tree on every size of the feature set. Besides, the performance of them was boosting with increasing of the feature set size.

• 디지털산업정보학회논문지
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• 제19권4호
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• pp.127-136
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• 2023

Many research results on the traffic enforcement of illegal driving of twowheeled motor vehicles using license plate recognition are introduced. Deep learning convolutional neural networks can be used for character and word recognition of license plates because of better generalization capability compared to traditional Backpropagation neural networks. In the plates of twowheeled motor vehicles, the interdependent government and city words are included. If we implement the mutually independent word recognizers using error correction rules for two word recognition results, efficient license plate recognition results can be derived. The CPU based convolutional neural network without library under real time processing has an advantage of low cost real application compared to GPU based convolutional neural network with library. In this paper twowheeled motor vehicle license plate recognition algorithm is introduced using CPU based deep-learning convolutional neural network. The experimental results show that the proposed plate recognizer has 96.2% success rate for outdoor twowheeled motor vehicle images in real time.

• 정보교육학회논문지
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• 제7권2호
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• pp.189-197
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• 2003

WBI(Web Based Instruction)는 교수 학습 자료의 개발 부담으로 특정 교과에 국한되어 있다. 본 논문은 WBI를 워드프로세서의 수업에 적용하여 인터넷 기반의 개별화된 교수-학습 시스템을 구현하였다. WBI를 적용한 워드프로세서 수업 방식은 전통적 수업 방식에 비해 학생들이 더욱 흥미를 느끼게 하고, 워드프로세서의 수준별, 능력별, 단계별 학습 선택으로 인해 학생 중심의 학습을 가능하게 하였다. 또한 개별학습 과제를 통해 학습내용을 실시간 평가 할 수 있으며 피드백이 가능하여 학습 효과를 극대화시킬 수 있었다.

• Journal of Computing Science and Engineering
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• 제10권4호
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• pp.103-110
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• 2016

Emotion recognition is an important component of affective computing, and is significant in the implementation of natural and friendly human-computer interaction. An effective approach to recognizing emotion from text is based on a machine learning technique, which deals with emotion recognition as a classification problem. However, in emotion recognition, the texts involved are usually very short, leaving a very large, sparse feature space, which decreases the performance of emotion classification. This paper proposes to resolve the problem of feature sparseness, and largely improve the emotion recognition performance from short texts by doing the following: representing short texts with word cluster features, offering a novel word clustering algorithm, and using a new feature weighting scheme. Emotion classification experiments were performed with different features and weighting schemes on a publicly available dataset. The experimental results suggest that the word cluster features and the proposed weighting scheme can partly resolve problems with feature sparseness and emotion recognition performance.