• Journal of the Korean Society for information Management
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• v.15 no.3
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• pp.73-93
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• 1998

Conventional full-text information retrieval system has been proved with high recall ratio and low precision ratio. One of the disadvantages of full-text IR system is that it is not designed to reflect the user's information need. It is due to the fact that full-text IR system has been designed based on physical and logical structure of document without considering the content of document. The purpose of the study is to develop more effective full-text IR system by resolving such disadvantages of conventional system. The study has developed new method of designing full-text IR system by using Content Structure Markup Language(CSML) other than conventioanal SGML.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.799-802
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• 2009

We have been studying the next generation of video creation solution based on TVML (TV program Making Language) technology. TVML is a well-known scripting language for computer animation and a TVML Player interprets the script to create video content using real-time 3DCG and synthesized voices. TVML has a long history proposed back in 1996 by NHK, however, the only available Player has been the one made by NHK for years. We have developed a new TVML Player from scratch and named it T2V (Text-To-Vision) Player. Due to the development from scratch, the code is compact, light and fast, and extendable and portable. Moreover, the new T2V Player performs not only a playback of TVML script but also a Text-To-Vision conversion from input written in XML format or just a mere plane text to videos by using 'Text-filter' that can be added as a plug-in of the Player. We plan to make it public as freeware from early 2009 in order to stimulate User-Generated-Content and a various kinds of services running on the Internet and media industry. We think that our T2V Player would be a key technology for upcoming new movement.

• IE interfaces
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• v.22 no.4
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• pp.329-335
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• 2009

Recently, the use of soft-keyboard is widespread and increases, because various handheld devices were developed such as PDA, navigation, mobile phones with enhanced competence of touchscreen. The use of soft-keyboard requires different characteristics compared to traditional hard-keyboard like QWERTY keyboard: no standard character layout, one finger entry, and cognitive processing time. In this study, therefore, the optimal soft-keyboard layout for one finger text entry in touchscreen environment was investigated among 6 keyboard layouts which were developed based on traditional characteristic of Korean text and the usage frequency of both vowels and consonants. As a result, the interface with Korean text invention order like 'ㄱㄴㄷㄹ' or 'ㅏㅑㅓㅕㅕ' was found to be better than the interface with usage frequency-based arrangement. Especially the vowels were most efficient when separated into two parts; located at the right-hand side and at right below the consonants. In conclusion, the keyboard layout with regard to the Korean text characteristic and the invention order was a more effective layout resulted from the minimum cognitive processing time.

• Journal of KIISE:Software and Applications
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• v.34 no.11
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• pp.984-990
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• 2007

As the amount of electronic documents increases explosively, automatic text categorization methods are needed to identify those of interest. Most methods use machine learning techniques based on a word set. This paper introduces a new method, called FPTC (FP-Tree based Text Classifier). FP-Tree is a data structure used in data-mining. In this paper, a method of storing text sentence patterns in the FP-Tree structure and classifying text using the patterns is presented. In the experiments conducted, we use our algorithm with a #Mutual Information and Entropy# approach to improve performance. We also present an analysis of the algorithm via an ordinary differential categorization method.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.408-410
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• 2021

In the NLP field, the pre-training model BERT launched by the Google team in 2018 has shown amazing results in various tasks in the NLP field. Subsequently, many variant models have been derived based on the original BERT, such as RoBERTa, ERNIEBERT and so on. In this paper, the WWMBERT (Whole Word Masking BERT) model suitable for Chinese text tasks was used as the baseline model of our experiment. The experiment is mainly for "Text-level Chinese text classification tasks" are improved, which mainly combines Tapt (Task-Adaptive Pretraining) and "Multi-Sample Dropout method" to improve the model, and compare the experimental results, experimental data sets and model scoring standards Both are consistent with the official WWMBERT model using Accuracy as the scoring standard. The official WWMBERT model uses the maximum and average values of multiple experimental results as the experimental scores. The development set was 97.70% (97.50%) on the "text-level Chinese text classification task". and 97.70% (97.50%) of the test set. After comparing the results of the experiments in this paper, the development set increased by 0.35% (0.5%) and the test set increased by 0.31% (0.48%). The original baseline model has been significantly improved.

• Journal of Electrical Engineering and information Science
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• v.1 no.2
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• pp.1-8
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• 1996

This paper presents a text-driven discourse analysis system, called DPAS. DPAS constructs a discourse structure by weaving together clauses in the text by finding discourse relations between a clause and the clauses in a context. The basic processing model of DPAS is based on the stack based model of discourse analysis suggested by Grosz and Sidner. We extend the model with dynamic programming method to handle various discourse ambiguities effectively and efficiently. We develop the idea of a context space to keep all information of a context. DPAS parses a text by considering all possible discourse relations between a clause and a context. Since different discourse relations may result in different states of a context, DPAS maintains multiple context spaces for an ambiguous text. Since maintaining all interpretations until the whole text is processed requires too much computing resources, DPAS uses the idea of depth-limited search to limit the search space. If there is more than one discourse relation between an input clause and a context, DPAS constructs context spaces one context space for each discourse relation. Then, DPAS applies heuristics to choose the most desirable context space after it processes some more input clauses. Since the basic idea of DPAS is domain independent, although we used descriptive texts to demonstrate DPAS, we believe the idea of DPAS can be extended to understand other styles of texts.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.1
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• pp.21-29
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• 2012

In this paper, we propose a new method to select colors representing the meaning of text contents based on the cognitive relation between words and colors, Our method is designed on the previous study revealing the existence of crucial words to estimate the colors associated with the meaning of text contents, Using the associative probability of each color with a given word and the strength of color association of the word, we estimate the probability of colors associated with a given text. The goal of this study is to propose a system to recommend the cognitively plausible colors for the meaning of the input text. To build a versatile and efficient database used by our system, two psychological experiments were conducted by using news site articles. In experiment 1, we collected 498 words which were chosen by the participants as having the strong association with color. Subsequently, we investigated which color was associated with each word in experiment 2. In addition to those data, we employed the estimated values of the strength of color association and the colors associated with the words included in a very large corpus of newspapers (approximately 130,000 words) based on the similarity between the words obtained by Latent Semantic Analysis (LSA). Therefore our method allows us to select colors for a large variety of words or sentences. Finally, we verified that our system cognitively succeeded in proposing the colors associated with the meaning of the input text, comparing the correct colors answered by participants with the estimated colors by our method. Our system is expected to be of use in various types of situations such as the data visualization, the information retrieval, the art or web pages design, and so on.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.79-84
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• 2009

This paper explores a method of text watermarking for agglutinative languages and develops a syntactic tree-based syntactic constituent movement scheme. Agglutinative languages provide a good ground for the syntactic tree-based natural language watermarking because syntactic constituent order is relatively free. Our proposed natural language watermarking method consists of seven procedures. First, we construct a syntactic dependency tree of unmarked text. Next, we perform clausal segmentation from the syntactic tree. Third, we choose target syntactic constituents, which will move within its clause. Fourth, we determine the movement direction of the target constituents. Then, we embed a watermark bit for each target constituent. Sixth, if the watermark bit does not coincide with the direction of the target constituent movement, we displace the target constituent in the syntactic tree. Finally, from the modified syntactic tree, we obtain a marked text. From the experimental results, we show that the coverage of our method is 91.53%, and the rate of unnatural sentences of marked text is 23.16%, which is better than that of previous systems. Experimental results also show that the marked text keeps the same style, and it has the same information without semantic distortion.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.121-126
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• 2006

In this paper, we propose an approach to increase the power of clustering Web images by using high-level semantic features from text information relevant to Web images as well as low-level visual features of image itself. These high-level text features can be obtained from image URLs and file names, page titles, hyperlinks, and surrounding text. As a clustering engine, self-organizing map (SOM) proposed by Kohonen is used. In the SOM-based clustering using high-level text features and low-level visual features, the 200 images from 10 categories are divided in some suitable clusters effectively. For the evaluation of clustering powers, we propose simple but novel measures indicating the degrees of scattering images from the same category, and degrees of accumulation of the same category images. From the experiment results, we find that the high-level text features are more useful in SOM-based Web image clustering.

• The Journal of Korean Association of Computer Education
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• v.5 no.3
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• pp.37-44
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• 2002

Automatic text classification is a task of assigning predefined categories to free text documents. Its importance is increased to organize and manage a huge amount of text data. There have been some researches on automatic text classification based on machine learning techniques. While most of them was focused on proposal of a new machine learning methods and cross evaluation between other systems, a through evaluation or optimization of a method has been rarely been done. In this paper, we propose an improving method of kNN-based Korean text classification system using heuristic informations about decision function, the number of nearest neighbor, and feature selection method. Experimental results showed that the system with similarity-weighted decision function, global method in considering neighbors, and DF/ICF feature selection was more accurate than simple kNN-based classifier. Also, we found out that the performance of the local method with well chosen k value was as high as that of the global method with much computational costs.