• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.462-467
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• 2003

As for control systems, many researchers give optimal structures of the finite-word-length compensator. D. Williamson solved a fixed-point case against colored noise for the LQG problem. Recently, one of the authors derived an optimal filter against colored noise. And consequently, we apply the result to a twodegree-of-freedom control system in this paper. In addition the perturbation of the coefficients is considered. Furthermore, simulation results indicate this method gives better than other structures.

• Proceedings of the Korean Society for Language and Information Conference
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• 2002.02a
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• pp.28-35
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• 2002

Automatic identification of Chinese personal names in unrestricted texts is a key task in Chinese word segmentation, and can affect other NLP tasks such as word segmentation and information retrieval, if it is not properly addressed. This paper (1) demonstrates the problems of Chinese personal name identification in some If applications, (2) analyzes the structure of Chinese personal names, and (3) further presents the relevant processing strategies. The geographical differences of Chinese personal names between Beijing and Hong Kong are highlighted at the end. It shows that variation in names across different Chinese communities constitutes a critical factor in designing Chinese personal name Identification algorithm.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.504-509
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• 2018

Neural machine translation (NMT) has recently achieved the state-of-the-art performance. However, it is reported failing in the word sense disambiguation (WSD) for several popular language pairs. In this paper, we explore the extent to which NMT systems are able to disambiguate the Korean homographs. Homographs, words with different meanings but the same written form, cause the word choice problems for NMT systems. Consistent with the popular language pairs, we discover that NMT systems fail to translate Korean homographs correctly. We provide a Korean word sense disambiguation tool-UTagger to use for improvement of NMT's translation quality. We conducted translation experiments using Korean-English and Korean-Vietnamese language pairs. The experimental results show that UTagger can significantly improve the translation quality of NMT in terms of the BLEU, TER, and DLRATIO evaluation metrics.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.87-92
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• 1998

In general, the synthesis unit database was constructed by recording isolated word. In that case, each boundary of word has typical prosodic pattern like a falling intonation or preboundary lengthening. To get natural synthetic speech using these kinds of database, we must artificially distort original speech. However, that artificial process rather resulted in unnatural, unintelligible synthetic speech due to the excessive prosodic modification on speech signal. To overcome these problems, we gathered thousands of sentences for synthesis database. To make a phone level synthesis unit, we trained speech recognizer with the recorded speech, and then segmented phone boundaries automatically. In addition, we used laryngo graph for the epoch detection. From the automatically generated synthesis database, we chose the best phone and directly concatenated it without any prosody processing. To select the best phone among multiple phone candidates, we used prosodic information such as break strength of word boundaries, phonetic contexts, cepstrum, pitch, energy, and phone duration. From the pilot test, we obtained some positive results.

• School Mathematics
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• v.11 no.4
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• pp.607-623
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• 2009

This study aims to investigate a child's informal knowledge of carrying and borrowing in additive calculations. The additive word problems including three types of calculations are posed a child that is the first grader and has no lessons about carrying and borrowing. By analysing his answers, his informal knowledge, that is his methods and strategies for calculating the additive problems are revealed. As a result, conceptual aspects and procedural aspects of his informal knowledge are recognized, and the didactical implications are induced for connecting his informal knowledge and the formal knowledge about carrying and borrowing.

• Korean Journal of Cognitive Science
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• v.22 no.2
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• pp.217-233
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• 2011

In this paper, an automatic word spacing system is proposed, which assumes sentences with no spaces between the words and segments them into proper words. Segmentation is regarded as a labeling problem in that segmentation can be done by attaching appropriate labels to each syllables of the given sentences. The system is based on Conditional Random Fields, which were reported to show excellent performance in labeling problems. The system is trained with a corpus of 1.12 million syllables, and evaluated with 2,114 sentences, 93 thousand syllables. The best results obtained are 98.84% of syllable-based accuracy and 95.99% of word-based accuracy.

• The Journal of Korean Association of Computer Education
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• v.17 no.6
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• pp.115-122
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• 2014

One of the problems with current youth generations is increasing rate of violence and suicide in their school lives, and this study aims at the design of a sentiment analysis system to prevent suicide by uising big data process. The main issues of the design are economical implementation, easy and fast processing for the users, so, the open source Hadoop system with MapReduce algorithm is used on the HDFS(Hadoop Distributed File System) for the experimentation. This study uses word count method to do the sentiment analysis with informal data on some sns communications concerning a kinds of violent words, in terms of text mining to avoid some expensive and complex statistical analysis methods.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.11
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• pp.63-75
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• 1998

Since many co-articulation problems are occurring in continuous spoken Korean language, several researches use words as a basic recognition unit. Though the word unit can solve this problem, it requires much memory and has difficulty fitting an input speech in a word list. In this paper, we propose an hidden Markov model(HMM) based recognition model that is an interconnection network of word HMMs for a syntax of sentences. To match suitably the input sentence into the continuous word list in the network, we use a level building search algorithm. This system represents the large sentence set with a relatively small memory and also has good extensibility. The experimental result of an airplane reservation system shows that it is proper method for a practical recognition system.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.115-125
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• 2006

A concept hierarchy represents the knowledge with multi-level form, which is very useful to categorize, store and retrieve the data. Traditionally, a concept hierarchy has been built manually by domain experts. However, the manual construction of a concept hierarchy has caused many problems such as enormous development and maintenance costs and human errors such as inconsistency. This paper proposes the automatic creation of concept hierarchies using the predefined hypernym relation. To create the hierarchy automatically, we first eliminate the ambiguity of the senses of data values, and construct the hierarchy by grouping and leveling of the remaining senses. We use the WordNet explanations for multi-meaning word to eliminate the ambiguity and use the WordNet hypernym relations to create multi-level hierarchy structure.

• Journal of Korea Multimedia Society
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• v.4 no.2
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• pp.171-181
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• 2001

The previous Bayesian document categorization method has problems that it requires a lot of time and effort in word clustering and it hardly reflects the semantic information between words. In this paper, we propose a weighted Bayesian document categorizing method based on association word knowledge base acquired by mining technique. The proposed method constructs weighted association word knowledge base using documents in training set. Then, classifier using Bayesian probability categorizes documents based on the constructed association word knowledge base. In order to evaluate performance of the proposed method, we compare our experimental results with those of weighted Bayesian document categorizing method using vocabulary dictionary by mutual information, weighted Bayesian document categorizing method, and simple Bayesian document categorizing method. The experimental result shows that weighted Bayesian categorizing method using association word knowledge base has improved performance 0.87% and 2.77% and 5.09% over weighted Bayesian categorizing method using vocabulary dictionary by mutual information and weighted Bayesian method and simple Bayesian method, respectively.