• The Research Journal of the Costume Culture
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• v.18 no.5
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• pp.977-990
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• 2010

Today, as individuals show their social identities and reflect their being as the members of society with a culture, an art style and communication function are stood out in fashion photographs. Accordingly, the meanings of images into text are expanded in its interpretative width through the acceptor's various terms. This researcher looked into four theories of both positions on the textuality of language and image, and considered the point of discussion on image of each theory through modern fashion photographs. First, the theory which divides language and image as auditory and visual recognitions in the textuality of language and image is limited from the view it focuses on only one side without considering the ambivalent elements of each field. For the textuality in modern fashion photographs, the observer attempts to turn it into text to give meaning to it as the recognition through five senses conforming to the acceptor's condition. Second, the theory dividing language and image into the text of time properties and spacial properties has limitation in the text, for acceptor's experience of the object appears as the structured form in time and space rather than being defined as two things like time and space. Third, the theory classifying the language and image text into conventional taste and natural taste has limitation from the view that image text is hardly an object of consistent classification in ease of recognition by the code accepted in society. Thus, this can't be fundamental approach for the understanding of the text of decoding trend represented in modern fashion photographs. Fourth, accordingly, this researcher focussed on contextual and arbitrary text of fashion photographs through the theory of Nelson Goodman which discusses image text through the differences in textuality. Basic mechanism of perceiving and recognizing and distinguish image is closely related to habit and custom like language. So, each acceptor perceives the image as a text through arbitrary interpretation obtained by individual, empirical, historical, and educational viewpoints. The textuality of modern fashion photographs aims to widen the range of diverse knowledge and understanding, transcending the regulations of simple function of existing fashion photographs. Consequently, this researcher puts forward the opinion of consistent and diverse follow-up studies on instilling meaning into fashion photographs for the understanding de-regulatory and de-constructive through various senses by avoiding only one sense-dependent fixed and regulatory properties of it.

• CRM연구
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• v.2 no.2
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• pp.53-70
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• 2009

Understanding consumer behavior based on the analysis of the customer data is one essential part of analytic CRM. To do this, the analytic skills for data extraction and data processing are required to users. As a user has various kinds of questions for the consumer data analysis, the user should use database language such as SQL. However, for the firm's user, to generate SQL statements is not easy because the accuracy of the query result is hugely influenced by the knowledge of work-site operation and the firm's database. This paper proposes a natural language based database search framework finding relevant database elements. Specifically, we describe how our TableRank method can understand the user's natural query language and provide proper relations and attributes of data records to the user. Through several experiments, it is supported that the TableRank provides accurate database elements related to the user's natural query. We also show that the close distance among relations in the database represents the high data connectivity which guarantees matching with a search query from a user.

• Journal of KIISE:Software and Applications
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• v.37 no.2
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• pp.129-136
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• 2010

In this paper, we present an Example-based Dialogue System for English conversation tutoring. It aims to provide intelligent one-to-one English conversation tutoring instead of old fashioned language education with static multimedia materials. This system can understand poor expressions of students and it enables green hands to engage in a dialogue in spite of their poor linguistic ability, which gives students interesting motivation to learn a foreign language. And this system also has educational functionalities to improve the linguistic ability. To achieve these goals, we have developed a statistical natural language understanding module for understanding poor expressions and an example-based dialogue manager with high domain scalability and several effective tutoring methods.

• Trans-
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• v.16
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• pp.107-133
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• 2024

Narratives are fundamental to human cognition and social culture, serving as the primary means by which individuals and societies construct meaning, share experiences, and convey cultural and moral values. The field of artificial intelligence, which seeks to mimic human thought and behavior, has long studied story generation and story understanding, and today's Large Language Models are demonstrating remarkable narrative capabilities based on advances in natural language processing. This situation raises a variety of changes and new issues, but a comprehensive discussion of them is hard to find. This paper aims to provide a holistic view of the current state and future changes by exploring the intersections and interactions of human and AI narrative intelligence. This paper begins with a review of multidisciplinary research on the intrinsic relationship between humans and narrative, represented by the term Homo narrans, and then provide a historical overview of how narrative has been studied in the field of AI. This paper then explore the possibilities and limitations of narrative intelligence as revealed by today's Large Language Models, and present three philosophical challenges for understanding the implications of AI with narrative intelligence.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.1009-1017
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• 2022

Log data has been used as a basis in understanding and deciding the main functions and state of information systems. It has also been used as an important input for the various applications in cybersecurity. It is an essential part to get necessary information from log data, to make a decision with the information, and to take a suitable countermeasure according to the information for protecting and operating systems in stability and reliability, but due to the explosive increase of various types and amounts of log, it is quite challenging to effectively and efficiently deal with the problem using existing tools. Therefore, this study has suggested a multiclass classification of the security severity level of multi-source event log using machine learning based on natural language processing. The experimental results with the training and test samples of 472,972 show that our approach has archived the accuracy of 99.59%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.81-83
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• 2022

Understanding research trends is necessary to select research topics and explore related works. Most researchers search representative keywords of interesting domains or technologies to understand research trends. However some conferences in artificial intelligence or data mining fields recently publish hundreds to thousands of papers for each year. It makes difficult for researchers to understand research trend of interesting domains. In our paper, we propose an automatic technology keyphrase extraction method to support researcher to understand research trend for each conference or journal. Keyphrase extraction that extracts important terms or phrases from a text, is a fundamental technology for a natural language processing such as summarization or searching, etc. Previous keyphrase extraction technologies based on pretrained language model extract keyphrases from long texts so performances are degraded in short texts like titles of papers. In this paper, we propose a techonolgy keyphrase extraction model that is robust in short text and considers the importance of the word.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.313-326
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• 2020

Deep Learning is the most important key to the development of Artificial Intelligence (AI). There are several distinguishable architectures of neural networks such as MLP, CNN, and RNN. Among them, we try to understand one of the main architectures called Recurrent Neural Network (RNN) that differs from other networks in handling sequential data, including time series and texts. As one of the main tasks recently in Natural Language Processing (NLP), we consider Neural Machine Translation (NMT) using RNNs. We also summarize fundamental structures of the recurrent networks, and some topics of representing natural words to reasonable numeric vectors. We organize topics to understand estimation procedures from representing input source sequences to predict target translated sequences. In addition, we apply multiple translation models with Gated Recurrent Unites (GRUs) in Keras on English-Korean sentences that contain about 26,000 pairwise sequences in total from two different corpora, colloquialism and news. We verified some crucial factors that influence the quality of training. We found that loss decreases with more recurrent dimensions and using bidirectional RNN in the encoder when dealing with short sequences. We also computed BLEU scores which are the main measures of the translation performance, and compared them with the score from Google Translate using the same test sentences. We sum up some difficulties when training a proper translation model as well as dealing with Korean language. The use of Keras in Python for overall tasks from processing raw texts to evaluating the translation model also allows us to include some useful functions and vocabulary libraries as well.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.60-65
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• 2013

Introducing the concept of construction safety in the design/engineering phase can improve the efficiency and effectiveness of safety management on construction sites. In this sense, further improvements for safety can be made in the design/engineering phase through the development of (1) an automated hazard identification process that is little dependent on user knowledge, (2) an automated construction schedule generation to accommodate varying hazard information over time, and (3) a visual representation of the results that is easy to understand. In this paper, we formulate an automated hazard identification framework for construction safety by extracting hazard information from related regulations to eliminate human interventions, and by utilizing a visualization technique in order to enhance users' understanding on hazard information. First, the hazard information is automatically extracted from textual safety and health regulations (i.e., Occupational Safety Health Administration (OSHA) Standards) by using natural language processing (NLP) techniques without users' interpretations. Next, scheduling and sequencing of the construction activities are automatically generated with regard to the 3D building model. Then, the extracted hazard information is integrated into the geometry data of construction elements in the industry foundation class (IFC) building model using a conformity-checking algorithm within the open source 3D computer graphics software. Preliminary results demonstrate that this approach is advantageous in that it can be used in the design/engineering phases of construction without the manual interpretation of safety experts, facilitating the designers' and engineers' proactive consideration for improving safety management.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.123-131
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• 2020

Along with the rapid development of artificial intelligence technology, natural language processing, which deals with human language, is also actively studied. In particular, BERT, a language model recently proposed by Google, has been performing well in many areas of natural language processing by providing pre-trained model using a large number of corpus. Although BERT supports multilingual model, we should use the pre-trained model using large amounts of Korean corpus because there are limitations when we apply the original pre-trained BERT model directly to Korean. Also, text contains not only vocabulary, grammar, but contextual meanings such as the relation between the front and the rear, and situation. In the existing natural language processing field, research has been conducted mainly on vocabulary or grammatical meaning. Accurate identification of contextual information embedded in text plays an important role in understanding context. Knowledge graphs, which are linked using the relationship of words, have the advantage of being able to learn context easily from computer. In this paper, we propose a system to extract Korean contextual information using pre-trained BERT model with Korean language corpus and knowledge graph. We build models that can extract person, relationship, emotion, space, and time information that is important in the text and validate the proposed system through experiments.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.51-57
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• 2020

Children with language developmental disabilities often struggle through their lives from a lot of challenges in everyday life and social activities. They're often easily deprived of the opportunity to engage in social activities, because they find difficulty in understanding or using language, a core means of communication. With regard to this issue, AAC(Augmentative and Alternative Communication) can be an effective communication tool for children who are suffering from language disabilities. In this paper, we propose a deep learning-based AI service to make full use of the pictogram as an AAC tool for children with language developmental disabilities to improve not only the ability to interact with others but the capacity to understand language. Using this service, we strive to help these children to more effectively communicate their intention or desire and enhance the quality of life.