• Proceedings of the Korean Society for Cognitive Science Conference
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• 2002.05a
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• pp.111-116
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• 2002

The regularity effect for printed word recognition and naming depends on ambiguities between single letters (small grain-size) and their phonemic values. As a given word is repeated and becomes more familiar, letter-aggregate size (grain-size) is predicted to increase, thereby decreasing the ambiguity between spelling pattern and phonological representation and, therefore, decreasing the regularity effect. Lexical decision and naming tasks studied the effect of repetition on the regularity effect for words. The familiarity of a word from was manipulated by presenting low and high frequency words as well as by presenting half the stimuli in mixed upper- and lowercase letters (an unfamiliar form) and half in uniform case. In lexical decision, the regularity effect was initially strong for low frequency words but became null after two presentations; in naming it was also initially strong but was merely reduced (although still substantial) after three repetitions. Mixed case words were recognized and named more slowly and tended to show stronger regularity effects. The results were consistent with the primary hypothesis that familiar word forms are read faster because they are processed at a larger grain-size, which requires fewer operations to achieve lexical selection. Results are discussed in terms of a neurobiological model of word recognition based on brain imaging studies.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.739-754
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• 2022

Usually, text data consists of many variables, and some of them are closely correlated. Such multi-collinearity often results in inefficient or inaccurate statistical analysis. For supervised learning, one can select features by examining the relationship between target variables and explanatory variables. On the other hand, for unsupervised learning, since target variables are absent, one cannot use such a feature selection procedure as in supervised learning. In this study, we propose a word selection procedure that employs topic models to find latent topics. We substitute topics for the target variables and select terms which show high relevance for each topic. Applying the procedure to real data, we found that the proposed word selection procedure can give clear topic interpretation by removing high-frequency words prevalent in various topics. In addition, we observed that, by applying the selected variables to the classifiers such as naïve Bayes classifiers and support vector machines, the proposed feature selection procedure gives results comparable to those obtained by using class label information.

• Proceedings of the Korean Society for Language and Information Conference
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• 1995.02a
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• pp.109-114
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• 1995

• Journal of Digital Convergence
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• v.16 no.8
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• pp.269-278
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• 2018

This study analyzed the outcome of 120 persons who used mobile game in order to investigate the influence that heuristic type which affects users' selection has on word of mouth, the influence that word of mouth has on users' behavioral intention and whether word of mouth will play the role of mediation between heuristic type and behavioral intention or not. First, heuristic(representatives, adjustment) type is found to have positive(+) influence on word of mouth (favorableness, reliance, persuasiveness). Second, while reliance word of mouth is found to have significant influence on behavioral intention, favorableness, persuasiveness and word of mouth is not found to have significant influence on behavioral intention. Third, heuristic (representative, adjustment) type is found to have significant influence on behavioral intention. Fourth, only reliance, word of mouth is found to play the role of mediation in the role of heuristic type and behavioral intention. The outcome of this study will help to understand users' behavioral intention selecting game in mobile game market.

• Proceedings of the KSPS conference
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• 2006.11a
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• pp.99-108
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• 2006

Three experiments were conducted to determine the exact locus of the frequency effect in speech production. In Experiment 1. a picture naming task was used to replicate whether the word frequency effect is due to the processes involved in lexical access or not. The robust word frequency effect of 31ms was obtained. The question to be addressed in Experiment 2 is whether the word frequency effect is originated from the level where a lemma is selected. To the end, using a picture-word interference task, the significance of interactions between the effects of target frequency, distractor frequency and semantic relatedness were tested. Interaction between the distractor frequency and semantic relatedness variables was significant. And interaction between the target and distractor frequency variables showed a significant tendency. In addition, the results of Experiment 2 suggest that the mechanism underlying the word frequency effect is encoded as different resting activation level of lemmas. Experiment 3 explored whether the word frequency effect is attributed to the lexeme level where phonological information of words is represented or not. A methodological logic applied to Experiment 3 was the same as to Experiment 2. Any interaction was not significant. In conclusion, the present study obtained the evidence supporting two assumptions: (a) the locus of the word frequency effect exists in the processes involved in lemma selection, (b) the mechanism for the word frequency effect is encoded as different resting activation level of lemmas. In order to explain the word frequency effect obtained in this study, the core assumptions of current production models need to be modified.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.327-332
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• 2010

Word mismatch is the most significant problem that causes low performance in question classification, whose questions consist of only two or three words that expressed in many different ways. So, it is necessary to apply word association in question classification. In this paper, we propose question classification method using translation-based language model, which use word translation probabilities for question-question pair that is learned in the same category. In the experiment, we prove that translation probabilities of question-question pairs in the same category is more effective than question-answer pairs in total collection.

• Journal of KIISE:Software and Applications
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• v.34 no.9
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• pp.804-816
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• 2007

Sample training data for machine learning often contain irrelevant information or redundant concept. It is also the case that the original data may include noise. If the information collected for constructing learning model is not reliable, it is difficult to obtain accurate information. So the system attempts to find relations or regulations between features and categories in the teaming phase. The feature selection is to remove irrelevant or redundant information before constructing teaming model. for improving its performance. Existing feature selection methods assume that the distribution of documents is balanced in terms of the number of documents for each class and the length of each document. In practice, however, it is difficult not only to prepare a set of documents with almost equal length, but also to define a number of classes with fixed number of document elements. In this paper, we propose a new feature selection method that considers the impurities among the words and unbalanced distribution of documents in categories. We could obtain feature candidates using the word impurity and eventually select the features through unbalanced distribution of documents. We demonstrate that our method performs better than other existing methods via some experiments.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.301-311
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• 2011

This paper is aimed at providing fundamental data for marketing strategies needed to determine the direction of policy to operate hospitals in an effective way. From the analysis, it was found that: first, expectation toward hospitality of the staff, facilities, environment and administrative service of hospitals for the elderly exercises significantly positive(+) effects on satisfaction of patients; second, trust in and satisfaction with administrative service of hospitals for the elderly exerts significantly positive(+) effects on the intent of patients for revisit and information by word of mouth; and third, the intent for revisit influences positively the intent for information by word of mouth significantly. It is therefore needed to try to understand the motives and tendencies of the elderly in selecting hospitals, to promote their satisfaction and to cultivate the spirit for patient-oriented service. Furthermore, efforts should be made to gain trust of medical staff, effects of medical treatment as well as medical equipment in addition to laborious endeavors to work out marketing strategies backed up by price competitiveness of paradigm.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.376-379
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• 2018

본 논문의 목적은 오픈 소스로 공개된 3가지 한국어 형태소 분석기 (kkma, twitter 및 mecab-ko)를 비교해서 한국어 자연어 처리에 가장 적합한 분석기를 선정하는 것이다. 이를 위해, 자연어 처리 분야에서 중요한 단어 임베딩 방법론 중 하나인 word2vec 모델의 성능 검증 방법을 사용해서 각 형태소 분석기의 성능을 정량적으로 비교했다. 그 결과 mecab-ko 형태소 분석기가 최적임이 확인되었다. 단 성능 검증에 사용된 어휘가 오직 명사뿐이라는 한계가 있으므로, 향후 연구에서는 좀 더 다양한 품사에 대한 성능검증이 필요할 것으로 보인다.

• Journal of Fashion Business
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• v.14 no.6
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• pp.11-22
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• 2010

The main purpose of this research was to examine the linguistic characteristics of domestic men's formal wear brand name. Four linguistic characteristics of language type, combined structure type of language, word class, length of brand name were investigated in this research and also examined the difference between brand type. For sample selection, the 209 men's fashion brands were selected from '2009 Korea Fashion Yearbook' and then, 25 brands which could not collect proper informations about the brand name or naming were excluded. Among total 184 men's brand names, 66 men's formal wear brands were selected and studied. For data analysis, quantitative evaluation of the frequency and qualitative evaluation have been used. The result as follows.; (1) Seven language types were found in domestic men's formal wear brand names. English has been used the most, then followed by Italian and French. (2) For combined structure type of brand name language, the single word used the most, followed by separately combined word type, artificially combined word, and unified word type. (3) The most frequently used the type of word class was noun, and followed by phrase, adjective, and verb. In the noun type, 6 different types which expressed a person, concrete & abstract entity, place, acronym, and neologic were found. For phrase, only noun type was appeared, however, 6 out of 20 phrases were abbreviated type. All eight adjective brand names implied an attributive character of the brand such as 'Dainty' or 'Solus(Solo)'. (4) The long name used most and then followed by normal and short length of brand name. Looking by the number of syllable, 4 syllables appeared the most and then followed by 3, 5, 6, 2 & 7 showed the same rate, and 8 syllables. (5) The result which compared the difference according to each brand type showed a difference in its language type, language combined style, word class, but length of brand name.