• Proceedings of the Korean Information Science Society Conference
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• 2007.10a
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• pp.42-43
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• 2007

• Journal of KIISE:Software and Applications
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• v.26 no.1
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• pp.178-178
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• 1999

Most stochastic taggers can not resolve some morphological ambiguities that can be resolved only by referring to lexical contexts because they use only contextual probabilities based ontag n-grams and lexical probabilities. Existing lexical rules are effective for resolving such ambiguitiesbecause they can refer to lexical contexts. However, they have two limitations. One is that humanexperts tend to make erroneous rules because they are deterministic rules. Another is that it is hardand time-consuming to acquire rules because they should be manually acquired. In this paper, wepropose context-dependent lexical rules, which are lexical rules based on the statistics of a taggedcorpus, and an ambiguity-driven teaming method, which is the method of automatically acquiring theproposed rules from a tagged corpus. By using the proposed rules, the proposed tagger can partiallyannotate an unseen corpus with high accuracy because it is a kind of memorizing tagger that canannotate a training corpus with 100% accuracy. So, the proposed tagger is useful to improve theaccuracy of a stochastic tagger. And also, it is effectively used for detecting and correcting taggingerrors in a manually tagged corpus. Moreover, the experimental results show that the proposed methodis also effective for English part-of-speech tagging.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.399-406
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• 2021

With the steady growth of the content industry, the need for research that automatically recommending content suitable for individual tastes is increasing. In order to improve the accuracy of automatic content recommendation, it is needed to fuse existing recommendation techniques using users' preference history for contents along with recommendation techniques using content metadata or features extracted from the content itself. In this work, we propose a new graph-based music recommendation method which learns an LSTM-based classification model to automatically extract appropriate tagging words from sound data and apply the extracted tagging words together with the users' preferred music lists and music metadata to graph-based music recommendation. Experimental results show that the proposed method outperforms existing recommendation methods in terms of the recommendation accuracy.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.157-177
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• 2008

Even in a single day, an enormous amount of content including digital videos, posts, photographs, and wikis are generated on the web. It's getting more difficult to recommend to a user what he/she prefers among these contents because of the difficulty of automatically grasping of content's meanings. CF (Collaborative Filtering) is one of useful methods to recommend proper content to a user under these situations because the filtering process is only based on historical information about whether or not a target user has preferred an item before. Collaborative Tagging is the process that allows many users to annotate content with descriptive tags. Recommendation using tags can partially improve, such as the limitations of CF, the sparsity and cold-start problem. In this research, a CF method with user-created tags is proposed. Collaborative tagging is employed to grasp and filter users' preferences for items. Empirical demonstrations using real dataset from del.icio.us show that our algorithm obtains improved performance, compared with existing works.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.374-385
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• 2008

Speech act tagging is an important step in various dialogue applications, which recognizes speaker's intentions expressed in natural language utterances. Previous approaches such as rule-based and statistics-based methods utilize the speech acts of previous utterances and sentence features of the current utterance. This paper proposes a method that determines speech acts of the current utterance using the speech acts of the following utterances as well as previous ones. Using the features of following utterances yields the accuracy 95.27%, improving previous methods by 3.65%. Moreover, sentence features of the previous utterances are employed to maximally utilize the information available to the current utterance. By applying the proper probability model for each speech act, final accuracy of 97.97% is achieved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.737-743
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• 2017

The intelligent life-log system proposed in this paper is intended to identify and record a myriad of everyday life information as to the occurrence of various events based on when, where, with whom, what and how, that is, a wide variety of contextual information involving person, scene, ages, emotion, relation, state, location, moving route, etc. with a unique tag on each piece of such information and to allow users to get a quick and easy access to such information. Context awareness generates and classifies information on a tag unit basis using the auto-tagging technology and biometrics recognition technology and builds a situation information database. In this paper, we developed an active modeling method and an application that recognizes expressionless and smile expressions using lip lines to automatically record emotion information.

• Korean Journal of Plant Tissue Culture
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• v.21 no.6
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• pp.319-326
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• 1994

For the gene tagging of Armoracia rusticana, maize controlling element Ac and Ds were introduced into A.rusticana via Agrobacterium-mediated transformation method. We established an efficient in via regeneration and transformation system for gene transfer in A. rusticana. The optimum in via regeneration condition has been obtained from leaf, petiole and root organs on modified MS medium supplemented with NAA 0.1 mg/L plus BA 1.0 mg/L for direct shooting and with free growth regulators for root induction for transformation, the leaf, petiole and root explants of A. rusticana were concultivated with Agrobacterium tumefaciens, LBA4404 which carries a binary vector pEND4K containing maize controlling element Ac or Ds, respectively: Selections were performed in the shoot induction medium supplemented with 100 mg/L kanamycin, and 500 mg/L carbenicillin transformation frequency showed about 8 to 10% in case of leaf disks. PCR md Southern blot analyses showed that the Ac and the Ds elements were integrated into the chromosome of donor plants.

• Mass Spectrometry Letters
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• v.1 no.1
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• pp.25-28
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• 2010

The high-throughput identification and accurate quantification of proteins are essential strategies for exploring cellular functions and processes in quantitative proteomics. Stable isotope tagging is a key technique in quantitative proteomic research, accompanied by automated tandem mass spectrometry. For the differential proteome analysis of mouse neuronal cell lines, we used a multiplexed isobaric tagging method, in which a four-plex set of amine-reactive isobaric tags are available for peptide derivatization. Using the four-plex set of isobaric tag for relative and absolute quantitation (iTRAQ) reagents, we analyzed the differential proteome in several stroke time pathways (0, 4, and 8 h) after the mouse neuronal cells have been stressed using a glutamate oxidant. In order to obtain a list of the differentially expressed proteins, we selected those proteins which had apparently changed significantly during the stress test. With 95% of the peptides showing only a small variation in quantity before and after the test, we obtained a list of eight up-regulated and four down-regulated proteins for the stroke time pathways. To validate the iTRAQ approach, we studied the use of oxidant stresses for mouse neuronal cell samples that have shown differential proteome in several stroke time pathways (0, 4, and 8 h). Results suggest that histone H1 might be the key protein in the oxidative injury caused by glutamate-induced cytotoxicity in HT22 cells.

• Journal of KIISE:Software and Applications
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• v.36 no.9
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• pp.749-757
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• 2009

A Internet user can easily access to the target information by web searching using some key-words or categories in the present Internet environment. When some meta-data which represent attributes of several data structures well are used, then more accurate result which is matched with the intention of users can be provided. This study proposes a multiple dimensional vector tagging method for the small web user group who interest in maintaining and sharing the bookmark for common interesting topics. The proposed method uses vector tag method for increasing the effect of categorization, management, and retrieval of target information. The vector tag composes with two or more components of the user defined priority. The basic vector space is created time of information and reference value. The calculated vector value shows the usability of information and became the metric of ranking. The ranking accuracy of the proposed method compares with that of a simply link structure, The proposed method shows better results for corresponding the intention of users.

• The KIPS Transactions:PartB
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• v.18B no.1
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• pp.45-50
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• 2011

Unknown morpheme errors in Korean morphological analysis are divided into two types: The one is the errors that a morphological analyzer entirely fails to return any morpheme sequences, and the other is the errors that a morphological analyzer returns incorrect combinations of known morphemes. Most previous unknown morpheme estimation techniques have been focused on only the former errors. This paper proposes a unknown morpheme estimation method which can handle both of the unknown morpheme errors. The proposed method detects Eojeols (Korean spacing units) that may include unknown morpheme errors using SVM (Support Vector Machine). Then, using CRFs (Conditional Random Fields), it segments morphemes from the detected Eojeols and annotates the segmented morphemes with new POS tags. In the experiments, the proposed method outperformed the conventional method based on the longest matching of functional words. Based on the experimental results, we knew that the second type errors should be dealt with in order to increase the performance of Korean morphological analysis.