• Journal of Service Research and Studies
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• v.11 no.2
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• pp.79-93
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• 2021

The size of the domestic art market has increased 21.9% over the past five years as of 2018 to KRW 448.2 billion and the number of transactions has also increased 31.6% to 39,367 points maintaining growth for the fifth consecutive year. Art distribution platforms are diversifying from galleries and auction-style offline to online auctions. The art market consists of three areas: production (creation), distribution (trade), and consumption (buying) of works and as the perception of artistic value as well as economic value spreads interest is also increasing as a means of investment. Consumers who purchase works and think of them as a means of investment technology have an increased need for objective information about their works, but there is a limit to collecting and analyzing objective and reliable statistics because information provision in the art market distribution area is closed and unbalanced. This paper identifies objective and reliable art distribution status and status through big data collection and structured and unstructured data analysis on art market distribution areas. Through this, we want to implement a system that can objectively provide analysis of authors in the current market. This study collected author information from art distribution sites and calculated the frequency of associated words by writer by collecting and analyzing the author's articles from Maeil Business, a daily newspaper. It aims to provide consumers with objective and reliable information.

• Information Systems Review
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• v.25 no.1
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• pp.93-110
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• 2023

Recently, attempts have been made to convert unstructured text into vectors and to analyze vast amounts of natural language for various purposes. In particular, the demand for analyzing texts in specialized domains is rapidly increasing. Therefore, studies are being conducted to analyze specialized and general-purpose documents simultaneously. To analyze specific terms with general terms, it is necessary to align the embedding space of the specific terms with the embedding space of the general terms. So far, attempts have been made to align the embedding of specific terms into the embedding space of general terms through a transformation matrix or mapping function. However, the linear transformation based on the transformation matrix showed a limitation in that it only works well in a local range. To overcome this limitation, various types of nonlinear vector alignment methods have been recently proposed. We propose a vector alignment model that matches the embedding space of specific terms to the embedding space of general terms through end-to-end learning that simultaneously learns the autoencoder and regression model. As a result of experiments with R&D documents in the "Healthcare" field, we confirmed the proposed methodology showed superior performance in terms of accuracy compared to the traditional model.

• Journal of Game and Entertainment
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• v.1 no.1
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• pp.39-46
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• 2005

Communication is an element of the story-telling in processing of the game as well as basic role of information exchange. In case of communication-based adventure games which are processed through communication, the communication is playing the role beyond its basic role up to providing a limited information on quests and solutions in Massively Multiplay Online Role Playing Game(MMORPG). Even though the communication is one of most important element for absorbtion into game, current communications with Non Playable Characters in games are just monotonous event-driven method following pattern based on prepared game scenario. In this research, we have analyzed natural language processing systems which are being used in the area of Artificial Intelligence for the purpose of more realistic communications in games, and have studied methods to apply these communications on games.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.77-97
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• 2017

A recent sharp increase of the biomedical literature causes researchers to struggle to grasp the current research trends and conduct creative studies based on the previous results. In order to alleviate their difficulties in keeping up with the latest scholarly trends, numerous attempts have been made to develop specialized analytic services that can provide direct, intuitive and formalized scholarly information by using various text mining technologies such as information extraction and event detection. This paper introduces and evaluates total 8 Convolutional Neural Network (CNN) models for extracting biomedical events from academic abstracts by applying various feature utilization approaches. Also, this paper conducts performance comparison evaluation for the proposed models. As a result of the comparison, we confirmed that the Entity-Type-Fully-Connected model, one of the introduced models in the paper, showed the most promising performance (72.09% in F-score) in the event classification task while it achieved a relatively low but comparable result (21.81%) in the entire event extraction process due to the imbalance problem of the training collections and event identify model's low performance.

• Korean Journal of Cognitive Science
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• v.19 no.3
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• pp.223-255
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• 2008

In this paper, we introduce an automatic system of scoring English essays. The system is comprised of three main components: a spelling checker, a grammar checker and a lexical cohesion checker. We have used such resources as WordNet, Link Grammar/parser and Roget's thesaurus for these components. The usefulness of an automatic scoring system depends on its reliability. To measure reliability, we compared the results of automatic scoring with those of manual scoring, on the basis of the Kappa statistics and the Multi-facet Rasch Model. The statistical data obtained from the comparison showed that the scoring system is as reliable as professional human graders. This system deals with textual units rather than sentential units and checks not only formal properties of a text but also its contents.

• The KIPS Transactions:PartD
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• v.8D no.1
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• pp.99-104
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• 2001

As a plan to counteract solving software crisis, the technology that is called Component Based Software Engineering (CBSE or Componentware) is introsuced. Component Based Software Developement (CBSD) Processes as one of the technique among CBSE have been proposed. Component specification activities among CBSD Processed are necessary and important for generating feusable component. However, the existing CBSD processed can't ensure the correctness in requirements are reflected. In this paper, we propose that formal activities wgich specify component using formal specification language Z which is verified with the ability of analysis and logicality. The proposed activities are composed of 5 tasks and 12 subtasks. By presenting specific 19 products, it is also proposed that component specification activities which are the base of CBSD for reusing. The proposed activities are able to improve qualities of component through correctness and verification of requirements specification of users in the early step of component-based software process.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.427-434
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• 2009

With the rapid evolution of the Internet and mobile environments, text including spelling errors such as newly-coined words and abbreviated words are widely used. These spelling errors make it difficult to develop NLP (natural language processing) applications because they decrease the readability of texts. To resolve this problem, we propose a spelling error correction model using a spelling error correction dictionary and a newspaper corpus. The proposed model has the advantage that the cost of data construction are not high because it uses a newspaper corpus, which we can easily obtain, as a training corpus. In addition, the proposed model has an advantage that additional external modules such as a morphological analyzer and a word-spacing error correction system are not required because it uses a simple string matching method based on a correction dictionary. In the experiments with a newspaper corpus and a short message corpus collected from real mobile phones, the proposed model has been shown good performances (a miss-correction rate of 7.3%, a F1-measure of 97.3%, and a false positive rate of 1.1%) in the various evaluation measures.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.40-46
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• 2020

With the development of the Internet, users share their experiences and opinions. Since related keywords are used witho0ut considering information such as the general emotion or genre of an unstructured document such as a movie review, the sensitivity accuracy according to the appropriate emotional situation is impaired. Therefore, we propose a system that predicts emotions based on information such as the genre to which the unstructured document created by users belongs or overall emotions. First, representative keyword related to emotion sets such as Joy, Anger, Fear, and Sadness are extracted from the unstructured document, and the normalized weights of the emotional feature words and information of the unstructured document are trained in a system that combines CNN and LSTM as a training set. Finally, by testing the refined words extracted through movie information, morpheme analyzer and n-gram, emoticons, and emojis, it was shown that the accuracy of emotion prediction using emotions and F-measure were improved. The proposed prediction system can predict sentiment appropriately according to the situation by avoiding the error of judging negative due to the use of sad words in sad movies and scary words in horror movies.

• Journal of The Korean Association of Information Education
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• v.9 no.4
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• pp.673-684
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• 2005

In this research, we have developed a "computer tutor" that mimics the human tutor with animated tutoring dialog agent and the agent was integrated to teaching-learning material for elementary science subject. The developed system is a natural language based teaching-learning system using one-to-one dialogue. The developed pedagogical dialogue teaching-learning system analysis student's answer then provides appropriate answer or questions after comparing the student's answer with elementary school level achievement. When the agent gives either question or answer it uses the TTS(Text-to-Speech) function. Also the agent has an animated human tutor face for providing more human like feedback. The developed dialogue interface has been applied to 64 6th grade students. The test results show that the test group's average score is higher than the control group by 10.797. This shows that unlike conventional web courseware, our approach that "ask-answer" process and the animated character, which has human tutor's emotional expression, attracts students and helps to immerse to the courseware.

• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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• pp.81-92
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• 2003

Generalized LR parsing is one of the enhanced LR parsing methods so that it overcome the limit of one-way linear stack of the traditional LR parser using graph-structured stack, and it has been playing an important role of a firm starting point to generate other variations for NL parsing equipped with various mechanisms. In this paper, we propose a conditional Action Model that can solve the problems of conventional probabilistic GLR methods. Previous probabilistic GLR parsers have used relatively limited contextual information for disambiguation due to the high complexity of internal GLR stack. Our proposed model uses Surface Phrasal Types representing the structural characteristics of the parse for its additional contextual information, so that more specified structural preferences can be reflected into the parser. Experimental results show that our GLR parser with the proposed Conditional Action Model outperforms the previous methods by about 6-7% without any lexical information, and our model can utilize the rich stack information for syntactic disambiguation of probabilistic LR parser.