• Korean Institute of Interior Design Journal
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• no.26
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• pp.88-94
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• 2001

This paper explores idea of the integration of text and image information in interior design. In this paper, we designed a structure of text and image information. Text information includes the information about materials and projects, and image information includes images of interior design. Material information consists of such as name and price of materials. Image information involves images of interior design that have been scanned and categorized into 15 groups according to the building regulation. Project information consists of construction brief and materials relevant to the image of interior design. The interior design information that is based on cases offers various information to designer and customers. In addition, the connection between text information and image information improves the quality of interior design by decreasing the trial and error in interior design processes. Finally, we discuss the method that integrates text and image information of interior design.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.863-875
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• 2017

The traditional text similarity measurement methods based on word frequency vector ignore the semantic relationships between words, which has become the obstacle to text similarity calculation, together with the high-dimensionality and sparsity of document vector. To address the problems, the improved singular value decomposition is used to reduce dimensionality and remove noises of the text representation model. The optimal number of singular values is analyzed and the semantic relevance between words can be calculated in constructed semantic space. An inverted index construction algorithm and the similarity definitions between vectors are proposed to calculate the similarity between two documents on the semantic level. The experimental results on benchmark corpus demonstrate that the proposed method promotes the evaluation metrics of F-measure.

• ETRI Journal
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• v.43 no.5
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• pp.856-868
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• 2021

Urdu is a widely spoken language in the Indian subcontinent with over 300 million speakers worldwide. However, linguistic advancements in Urdu are rare compared to those in other European and Asian languages. Therefore, by following Text Retrieval Conference standards, we attempted to construct an extensive text collection of 85 304 documents from diverse categories covering over 52 topics with relevance judgment sets at 100 pool depth. We also present several applications to demonstrate the effectiveness of our collection. Although this collection is primarily intended for text retrieval, it can also be used for named entity recognition, text summarization, and other linguistic applications with suitable modifications. Ours is the most extensive existing collection for the Urdu language, and it will be freely available for future research and academic education.

• Proceedings of the CALSEC Conference
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• 2004.02a
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• pp.294-301
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• 2004

·Text-mining approach to e-documents organization based on topic hierarchy - Machine-Learning ＆ information Theory-based ㆍ 'Category(topic) discovery' problem → document bundle-based user-constraint document clustering ㆍ 'Automatic categorization' problem → Accelerated EM with CU-based active learning → 'Hierarchy Construction' problem → Unsupervised learning of category subsumption relation

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.1082-1087
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• 1994

A set of text database is indispensable to the probabilistic models for speech recognition, linguistic model, and machine translation. We introduce an environment to canstruct text databases : an automatic tagging system and a set of tools for lexical knowledge acquisition, which provides the facilities of automatic part of speech recognition and guessing.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.205-222
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• 2022

This study aims to improve the information service so that construction engineers and construction project participants without specialized knowledge can easily understand the important words and the interrelationships between them in construction practice. To this end, using text mining and network centrality, the frequency of occurrence of words, topic modeling, and network centrality in construction practice information such as technical information, case information, and cost reduction, which are most used in the Construction Technology Digital Library, were analyzed. Through this analysis, design, construction, project management, specifications, standards, and maintenance related to road construction such as roads, pavements, bridges, and tunnels were identified as important in construction practice. In addition, correlations were analyzed for words with high importance by measuring Degree Centrality and Eigenvector Centrality. The result was that more useful information could be provided if the technical information was expanded. Finally, we presented the limitations of the study results and additional studies according to the limitations.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.5_6
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• pp.169-175
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• 2007

Suffix arrays, fundamental full-text index data structures, can be efficiently used where patterns are queried many times. Although many useful full-text index data structures have been proposed, their O(nlogn)-bit space consumption motivates researchers to develop more space-efficient ones. However, their space efficient versions such as the compressed suffix array and the FM-index have been developed; those can not reduce the practical working space because their constructions are based on the existing suffix array. Recently, two direct construction algorithms of compressed suffix arrays from the text without constructing the suffix array have been proposed. In this paper, we compare practical performance of these algorithms of compressed suffix arrays with that of various algorithms of suffix arrays by measuring the construction times, the peak memory usages during construction and the sizes of their final outputs.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1220-1229
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• 2024

In the construction industry, professionals are required to have advanced problem-solving skills to adeptly handle the dynamic challenges inherent to project execution. These skills are crucial, as they enable professionals to effectively navigate the complexities and unpredictability of construction projects, ensuring timely and cost-effective completion. This paper explores an innovative approach to enhance the problem-solving skills of construction students through the integration of conversational AI-based serious games into their educational curriculum. The objective of this research was acquired by following three phases: hazard interaction, problem identification, and AI-guided text-based communication. This approach creates an engaging learning environment, simulating real-world construction challenges and problems, focusing on the excavation phase of a construction project as a case study for students to interact with and communicate with the Conversational AI agent through text-based prompts. In the future, the proposed study can be used to evaluate how AI agents can help enhance problem-solving skills by promoting emotional engagement among participants. This research sheds light on the potential of integrating conversational AI in education, providing valuable insights for educators designing construction management training programs by underscoring the importance of engagement in real-world problem-solving scenarios.

• Proceedings of the Korean Society for Language and Information Conference
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• 2002.06a
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• pp.87-97
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• 2002

No Abstract(See Full Text)

• Proceedings of the Zoological Society Korea Conference
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• 1995.10b
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• pp.343.1-343
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• 1995

No Abstract, See Full Text