• Journal of KIISE:Software and Applications
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• v.41 no.7
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• pp.523-532
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• 2014

In this paper, we propose a lattice-based discriminative approach for Korean morphological analysis and POS tagging. In our approach, for an input sentence, a morpheme lattice is first created from a lexicon where each node corresponds to a morpheme in the lexicon and each edge is formed between two consecutive morphemes. A candidate result of morphological analysis is then represented as a path in the morpheme lattice which is defined as the sequence of edges, starting in the initial state and ending with the final state. In this setting, the morphological analysis is simply considered as the process of finding the best path among all possible paths. Experiment results show that the proposed lattice-based method outperforms the first-order linear-chain CRF.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.133-141
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• 2016

Recent web services provide tagging function to users, and let them express the topic of the contents of their articles. Moreover, we can extract context information like emotion of the writer efficiently by using tags attached to the articles or images. And we are able to better understand article than traditional algorithm. (eg. TF-IDF) Therefore, if we use tags in recommendation system, we can recommend high quality resources to the users. This study proposes a recommendation method that provide web resources (articles, users) through simple algorithm based on related tag set extracted from the article. Through the experiments, we show that the result was satisfactory, and we measure the satisfaction of users.

• Journal of KIISE
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• v.41 no.12
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• pp.1117-1125
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• 2014

Folksonomy data, which is derived from social tagging systems, is a useful source for understanding a user's intention and interest. Using the folksonomy data, it is possible to create an accurate user profile which can be utilized to build a personalized search system. However there are limitations in some of the traditional methods such as Vector Space Model(VSM) for user profiling and similarity computation. This paper suggests a novel method with graph-based user and document profile which uses the proximity information of query terms to improve personalized search. We demonstrate the performance of the suggested method by comparing its performance with several state-of-the-art VSM based personalization models in two different folksonomy datasets. The results show that the proposed model constantly outperforms the other state-of-the-art personalization models. Furthermore, the parameter sensitivity results show that the proposed model is parameter-free in that it is not affected by the idiosyncratic nature of datasets.

• Korean Journal of Cognitive Science
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• v.24 no.2
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• pp.95-109
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• 2013

With the rapid evolution of the personal device environment, the demand for natural language applications is increasing. This paper proposes a morpheme segmentation and part-of-speech tagging model, which provides the first step module of natural language processing for many languages; the model is designed for mobile devices with limited hardware resources. To reduce the number of morpheme candidates in morphological analysis, the proposed model uses a method that adds highly possible morpheme candidates to the original outputs of a conventional left-longest-match-preference method. To reduce the computational cost and memory usage, the proposed model uses a method that simplifies the process of calculating the observation probability of a word consisting of one or more morphemes in a conventional hidden Markov model.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.123-124
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• 2010

• Korea-Australia Rheology Journal
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• v.14 no.3
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• pp.93-105
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• 2002

Experimental methods for making quantitative measurements of velocity fields in non-Newtonian fluids are reviewed. Techniques based on light scattering spectroscopy - laser Doppler velocimetry and homodyne light scattering spectroscopy, techniques based on imaging the displacement of markers - including particle image velocimetry and molecular tagging velocimetry, and techniques based on nuclear magnetic resonance imaging are discussed. The special advantages and disadvantages of each method are summarized, and their applications to non-Newtonian flows are briefly reviewed. Example data from each technique are also included.

• 대한핵의학회:학술대회논문집
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• 1971.11a
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• pp.87.1-87.1
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• 1971

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.1
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• pp.143-149
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• 2010

In ubiquitous environment, information retrieval using collaborative filtering is a popular technique for reducing information overload. Collaborative filtering systems can produce personal recommendations by computing the similarity between your preference and the one of other people. We integrate the collaboration filtering method and context-aware information retrieval method. The proposed method enables to find some relevant information to specific user's contexts. It aims to makes more effective information retrieval to the users. The proposed method is conceptually comprised of two main tasks. The first task is to tag context tags by automatic tagging technique. The second task is to recommend items for each user's contexts integrating collaborative filtering and information retrieval. We describe a new integration method algorithm and then present a u-commerce application prototype.

• Korean Journal of Plant Tissue Culture
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• v.27 no.4
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• pp.259-268
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• 2000

Senescence is a sequence of biochemical and physiological events that lead to death of a cell, organ, or whole organism. Senescence is now clearly regarded as a genetically determined and evolutionarilly acquired developmental process comprising the final stage of development. However, in spite of the biological and practical importance, genetic mechanism of senescence has been very limited. Through forward and reverse genetic approaches, we are trying to reveal the molecular and genetic mechanism of senescence in plants, employing leaf organs of Arabidopsis as a model system. Using forward genetic approach, we have initially isolated several delayed senescence mutants either from T-DNA insertional lines or chemical-mutagenized lines. In the case of ore 4 and ore 9 mutants, the mutated genes were identified. The recent progress on characterization of mutants and identification of the mutated genes will be reported. We are also screening mutations from other various sources of mutant pools, such as activation tagging lines and promoter trap lines. Two dominant senescence-delayed mutants were isolated from the activation tagging pool. Cloning of the genes responsible for this phenotype is in progress. For reverse genetic approach, the genes that induced during leaf senescence were first isolated by differential screening method. We are currently using PCR-based suppression subtractive hybridization, designed to enrich a cDNA library for rare differentially expressed transcripts. Using this method, we have identified over 35 new sequences that are upregulated at leaf senescence stage. We are investigating the function of these novel genes by systemically generating antisense lines.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.133-140
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• 2012

In this paper, we propose a music recommendation method considering users' tags by collaborative tagging in a social music site. Since collaborative tagging allows a user to add keywords chosen by himself to web resources, it provides users' preference about the web resources concretely. In particular, emotional tags which represent human's emotion contain users' musical preference more directly than factual tags which represent facts such as musical genre and artists. Therefore, to classify the tags into the emotional tags and the factual tags and to assign weighted values to the emotional tags, a tag ontology called UniTag is developed. After preprocessing the tags, the weighted tags are used to create user profiles, and the music recommendation algorithm is executed based on the profiles. To evaluate the proposed method, a conventional playcount-based recommendation, an unweighted tag-based recommendation, and an weighted tag-based recommendation are executed. Our experimental results show that the weighted tag-based recommendation outperforms other two approaches in terms of precision.