• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.986-998
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• 2006

There has been an increasing necessity of Semantic Web-based metadata that helps computers efficiently understand and manage an information increased with the growth of Internet. However, it seems inevitable to face some semantically ambiguous information when metadata is generated. Therefore, we need a solution to this problem. This paper proposes a new method for automated metadata generation with the help of a concept of class, in which some ambiguous words imbedded in information such as documents are semantically more related to others, by using probability model of consequent words. We considers ambiguities among defined concepts in ontology and uses the Hidden Markov Model to be aware of part of a named entity. First of all, we constrict a Markov Models a better understanding of the named entity of each class defined in ontology. Next, we generate the appropriate context from a text to understand the meaning of a semantically ambiguous word and solve the problem of ambiguities during generating metadata by searching the optimized the Markov Model corresponding to the sequence of words included in the context. We experiment with seven semantically ambiguous words that are extracted from computer science thesis. The experimental result demonstrates successful performance, the accuracy improved by about 18%, compared with SemTag, which has been known as an effective application for assigning a specific meaning to an ambiguous word based on its context.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.9-15
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• 2001

In this paper we investigate context dependent acoustic models to improve the performance of the Korean speech recognition . The algorithm are using the Korean phonological rules and decision tree, By Successive State Splitting(SSS) algorithm the Hidden Merkov Netwwork(HM-Net) which is an efficient representation of phoneme-context-dependent HMMs, can be generated automatically SSS is powerful technique to design topologies of tied-state HMMs but it doesn't treat unknown contexts in the training phoneme contexts environment adequately In addition it has some problem in the procedure of the contextual domain. In this paper we adopt a new state-clustering algorithm of SSS, called Phonetic Decision Tree-based SSS (PDT-SSS) which includes contexts splits based on the Korean phonological rules. This method combines advantages of both the decision tree clustering and SSS, and can generated highly accurate HM-Net that can express any contexts To verify the effectiveness of the adopted methods. the experiments are carried out using KLE 452 word database and YNU 200 sentence database. Through the Korean phoneme word and sentence recognition experiments. we proved that the new state-clustering algorithm produce better phoneme, word and continuous speech recognition accuracy than the conventional HMMs.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.74-77
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• 2007

In order to compare two hypotheses on the origin of semantic interference effect that has been offered in the psycholinguistic literature, we conducted two experiments using the picture-word interference paradigm. When participants named the pictures of the objects simultaneously presented with distractor words, they were required to use either native words (Experiment 1) or loanwords (Experiment 2). The pictures were paired with three kinds of distractor words that were identical, semantically related and neutral to the picture. Two observations were obtained from two experiments. Firstly, the naming times of the pictures were more fast in context of the identical distractors than in context of the neutral ones. Secondly, naming times were more slow in the presence of the semantically related distractors relative to the neutral ones. These findings support the claim that semantic interference is based on a lexical retrieval conflict.

• Asia pacific journal of information systems
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• v.28 no.1
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• pp.36-60
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• 2018

As an increasing number of people gained access to social network services (SNS), organizations started to use SNS as a channel for marketing and promotional purposes. The online advertising market has significant growth potential. Brand engagement is a key motive for online advertising, but how SNS users engage with brands, particularly in terms of the promotion of organizations, is poorly understood. This study uses customer value theory to examine brand engagement of users in terms of promoting companies in the context of Korean SNS marketing. This study identifies the antecedents of brand engagement based on customer value theory. Our findings show the significance of three factors of SNS marketing, namely, price discount, relationship support, and convenience, on brand engagement. We further show the consequences of brand engagement, namely, purchase decisions and word-of-mouth activities. These findings help advance customer value theory and offer practical insights into the use of information systems and marketing in the context of SNS.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.11-14
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• 1991

음소단위로 구성된 음소군들 각각에 대해 구성된 신경 회로망을 하나로 통합하는 모듈구조로 신경망을 이용하여 일반적인 예약 시스템에서 사용할 수 있는 어휘인 시간명, 월명, 지역명등 총 34 단어에 대한 인식 실험내용을 기술한다. 구문회로망(context net)를 이용하는 경우에 약 91.2%의 인식율을, 단순히 음소단위를 기반으로하여 인식할 경우에 약 72%의 인식율을 얻으므로써, 음소 단위 인식시스템의 경우에 보다 높은 인식율을 얻기 위해서는 상위 level의 처리가 수반되어야 함을 확인할 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.25-31
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• 2014

Korean grammar checkers typically detect context-dependent errors by employing heuristic rules; these rules are formulated by language experts and consisted of lexical items. Such grammar checkers, unfortunately, show low recall which is detection ratio of errors in the document. In order to resolve this shortcoming, a new error-decision rule-generalization method that utilizes the existing KorLex thesaurus, the Korean version of Princeton WordNet, is proposed. The method extracts noun classes from KorLex and generalizes error-decision rules from them using the Tree Cut Model and information-theory-based MDL (minimum description length).

• The Journal of the Acoustical Society of Korea
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• v.33 no.4
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• pp.268-272
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• 2014

This paper presents a method for speech recognition using multi-section vector-quantization (MSVQ) and time-delay recurrent neural network (TDTNN). The MSVQ generates the codebook with normalized uniform sections of voice signal, and the TDRNN performs the speech recognition using the MSVQ codebook. The TDRNN is a time-delay recurrent neural network classifier with two different representations of dynamic context: the time-delayed input nodes represent local dynamic context, while the recursive nodes are able to represent long-term dynamic context of voice signal. The cepstral PLP coefficients were used as speech features. In the speech recognition experiments, the MSVQ/TDRNN speech recognizer shows 97.9 % word recognition rate for speaker independent recognition.

• The Journal of Information Systems
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• v.19 no.1
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• pp.117-148
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• 2010

The user interface (UI) is the part of the systems that users can see, hear and feel. That means, a user can interact with the systems through the user interface. Effective user interfaces create positive feelings of success and competence. In addition, they enable users to concentrate on their work, exploration, or pleasure. The German word "Gestalt" means "whole" as well as a figure or a shape. A comprehensive information systems (IS) literature review found that there has been little empirical evidence on the Gestalt principles in UIs that affect online users' perceived usability in a Web context. Therefore, the goals of this study are to 1) examine the relationships between Gestalt principles in UIs and online users' perceived usability in a Web context and 2) come up with design guidelines to enhance user success. Using a survey, this study explored the relationships between Gestalt principles used in six Web sites and 17 aspects of usability. The results showed that in terms of 16 aspects of usability, there are significant differences among the UIs that have different levels of Gestalt principles. In practice, online stores may use these findings to improve their Web pages. Specifically, the sixteen usability attributes can be used to check whether online stores' Web pages meet customers' user interface quality expectations.

• The Journal of Korea Robotics Society
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• v.5 no.3
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• pp.240-250
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• 2010

A URC, which is a Ubiquitous Robot Companion, provides services to users in ubiquitous computing environments and has advantage of simplifying robot's hardware and software by distributing the complicated functionality of robots to other system. In this paper, we propose SOWL, which is a software architecture for URC robots and a mixed word of SOMAR and CAWL. SOWL keeps the advantages of URC and it also has the loosely-coupled characteristics. Moreover it makes it easy to develop of URC robot software. The proposed architecture is composed of 4 layers: device software, robot software, robot application, and end user layer. Developers of the each layer is able to build software suitable for their requirements by combining software modules in the lower layer. SOWL consists of SOMAR and CAWL engine. SOMAR, which is a middleware for the execution of device software and robot software, is based on service-oriented architecture(SOA) for robot software. CAWL engine is a system to process CAWL which is a context-aware workflow language. SOWL is able to provide a layered architecture for the execution of a robot software. It also makes it possible for developers of the each layer to build module-based robot software.

• ETRI Journal
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• v.41 no.2
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• pp.235-241
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• 2019

In this paper, we propose a rank-weighted reconstruction feature to improve the robustness of a feed-forward deep neural network (FFDNN)-based acoustic model. In the FFDNN-based acoustic model, an input feature is constructed by vectorizing a submatrix that is created by slicing the feature vectors of frames within a context window. In this type of feature construction, the appropriate context window size is important because it determines the amount of trivial or discriminative information, such as redundancy, or temporal context of the input features. However, we ascertained whether a single parameter is sufficiently able to control the quantity of information. Therefore, we investigated the input feature construction from the perspectives of rank and nullity, and proposed a rank-weighted reconstruction feature herein, that allows for the retention of speech information components and the reduction in trivial components. The proposed method was evaluated in the TIMIT phone recognition and Wall Street Journal (WSJ) domains. The proposed method reduced the phone error rate of the TIMIT domain from 18.4% to 18.0%, and the word error rate of the WSJ domain from 4.70% to 4.43%.