• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.1057-1064
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• 2007

From the view of natural language process, quantitative linguistic analysis is a linguistic study relying on statistical methods, and is a mathematical linguistics in an attempt to discover various linguistic characters by interpreting linguistic facts quantitatively through statistical methods. In this study, I would like to introduce a quantitative linguistic analysis method utilizing a computer and statistical methods on literary works. I also try to introduce a use of SynKDP, a synthesized Korean data process, and show the relations between distribution of linguistic unit elements which are used by the hero in a novel #Sassinamjunggi# and theme analysis on literary works.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.427-432
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• 2005

This paper is concerned with a simultaneous design of TSK (Takagi-Sugeno-Kang)-linguistic fuzzy models with uncertain model output and the computationally efficient representation. For this purpose, we use the fundamental idea of linguistic models introduced by Pedrycz and develop their comprehensive design framework. The design process consists of several main phases such as (a) the automatic generation of the linguistic contexts by probabilistic distribution using CDF (conditional density function) and PDF (probability density function) (b) performing context-based fuzzy clustering preserving homogeneity based on the concept of fuzzy granulation (c) augment of bias term to compensate bias error (d) combination of TSK and linguistic context in the consequent part. Finally, we contrast the performance of the enhanced models with other fuzzy models for automobile MPG predication data and coagulant dosing process in a water purification plant.

• 한국언어정보학회지:언어와정보
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• 제8권2호
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• pp.131-147
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• 2004

the two Korean causal conjunctive suffixes, -nula(ko) and -nun palamey, based on corpus linguistic analysis. Many of the linguistic accounts available, both in pedagogical reference and in the literature on linguistics, provide incomplete analyses of these suffixes, based on fabricated linguistic data. Using naturally occurring, real linguistic data, this paper examines the syntactic and semantic structures of the two causal suffixes through a consideration of three areas of corpus linguistic analysis: token frequencies, collocations, and semantic prosody. An analysis based on concordance data reveals that the two causal connectives, -nula(ko) and -nun palamey, have more differences than similarities in terms of syntactic and semantic constraints. The idiosyncratic structures of the two suffixes are discussed in terms of same subject condition, verb selection, same agent condition, synchronicity condition, and negative semantic prosody.

• 한국정보시스템학회지:정보시스템연구
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• 제31권4호
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• pp.01-23
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• 2022

Purpose This study aims to propose a deep learning-based fake review detection model by combining authors' linguistic features and semantic information of reviews. Design/methodology/approach This study used 358,071 review data of Yelp to develop fake review detection model. We employed linguistic inquiry and word count (LIWC) to extract 24 linguistic features of authors. Then we used deep learning architectures such as multilayer perceptron(MLP), long short-term memory(LSTM) and transformer to learn linguistic features and semantic features for fake review detection. Findings The results of our study show that detection models using both linguistic and semantic features outperformed other models using single type of features. In addition, this study confirmed that differences in linguistic features between fake reviewer and authentic reviewer are significant. That is, we found that linguistic features complement semantic information of reviews and further enhance predictive power of fake detection model.

• 소음진동
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• 제6권5호
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• pp.547-554
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• 1996

Diagnosis is one of the dominant applications of expert systems technology today. Most diagnosis system is apply to if-then rule, and it is called production systems which consist of linguistic data. A new diagnosis method is suggested in this paper, in which the fuzzy reasoning theory is used to diagnosis the rotating machinery. Diagnosis algorithm is made fuzzy reasoned by using linguistic data of fuzziness. Linguistic data for fuzziness was described in fuzzy scale and fuzzy membership function. Then, those lingnistic data have been synthesized and defuzzificated according to every item observed. This system is successfully used for linguistic data in fuzziness of rotating machinery. The results indicate that the realistic application can be built in precision diagnosis system.

• 한국산학기술학회논문지
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• 제14권8호
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• pp.3760-3767
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• 2013

본 연구는 초등학생들을 대상으로 일반창의성, 언어지능, 국어성적이 언어창의성에 미치는 영향을 살펴보고자 실시되었다. 서울시에 위치한 3개의 초등학교 4학년 338명을 대상으로 검사를 실시하였다. 사용된 검사는 다중지능검사 중 언어검사와 일반창의성 검사인 TTCT이며 국어성적은 학교에서 실시한 중간, 기말시험 성적을 사용하였으며 언어창의성은 에세이쓰기를 실시하였다. 수집된 자료는 SPSS 18.0을 활용하여 상관분석, 회귀분석, 분산분석을 실시하였다. 연구결과를 살펴보면 언어창의성은 일반창의성 전체점수에서 상관이 있는 것으로 나타났으며 하위요인에서는 제목의 추상성과 정교성에서 유의미한 상관이 나타났다. 언어창의성은 국어성적과는 정적상관이 있는 것으로 나타났으며 언어지능과는 상관이 없는 것으로 나타났다. 회귀분석을 실시한 결과 언어창의성은 일반창의성 가운데 제목의 추상성 요인이 가장 잘 설명하는 것으로 나타났으며 제목의 추상성 수준에 따라 언어창의성 평균은 의미있는 차이가 있는 것으로 나타났다.

• Journal of Smart Tourism
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• 제1권3호
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• pp.21-29
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• 2021

This study explored the relationship between post types and linguistic characteristics in marketer-generated content and social media engagement to find the optimized content to enhance social media engagement level. Post data of 23,588 marketer-generated content were collected from 50 states' destination marketing organization Facebook pages in the United States. The collected data were analyzed by employing social media analytics, linguistic analysis, multivariate analysis of variance, and discriminant analysis. The results showed that there are significant differences in both engagement indicators and linguistic scores among the three post types. Based on research findings, this research not only provided researchers with theoretical implications but also suggested practitioners the most effective content designs for travel destination marketing in Facebook.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2007년도 한국음성과학회 공동학술대회 발표논문집
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• pp.102-105
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• 2007

This study was designed to examination effects of concurrent linguistic or cognitive tasks on speech rate. Eight normal speakers were repeated sentences either with or without simultaneous a linguistic task and a cognitive task. Linguistic task was conducted by generating verbs from nouns and cognitive task meaned performing mental arithmetic. Speech rate was measured from acoustic data. One-way ANOVA conducted to know speech rate difference among 3 different type of tasks. The results showed there was no significant difference between sentence repeat and linguistic tasks. But There was significant difference findings: sentence repeat and linguistic task, linguistic and cognitive task.

• Journal of the Korean Data and Information Science Society
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• 제12권1호
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• pp.89-97
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• 2001

Chen and Chen(FSS, 1993, 159-168) presented a reasonable analytical model of fuzzy closed-loop systems and proposed a method to analyze the stability of fuzzy control by the relational matrix of fuzzy system. Chen, Lu and Chen(IEEE Trans. Syst. Man Cybern., 1995, 881-888) formulated the sufficient and necessary conditions on stability of fuzzy closed-loop control systems. Gang and Chen(FSS, 1996, 27-34) deduced a linguistic relation model of a fuzzy closed loop control system from the linguistic models of the fuzzy controller and the controlled process and discussed the linguistic stability of fuzzy closed loop system by a linguistic relation matrix. In this paper, we study more on their models. Indeed, we prove the existence and uniqueness of equilibrium state $X_e$ in which fuzzy system is stable and give closed form of $X_e$. The same examples in Chen and Chen and Gang and Chen are treated to analyze the stability of fuzzy control systems.

• 전자공학회논문지CI
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• 제47권3호
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• pp.76-82
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• 2010

본 논문은 수치적인 입출력데이터로부터 언어적인 규칙을 생성시키기 위한 체계적인 접근방법으로써 정보입자(information granules)에 근거한 언어적인 모델(LM: Linguistic Model)을 발전시킨다. Pedrycz에 의해 소개된 언어적인 모델은 컨텍스트 기반 퍼지 클러스터링(CFC: Context-based Fuzzy Clustering)으로부터 얻어지는 퍼지 정보입자에 의해 수행되어지며, 이는 입력과 출력공간과 연관된 클러스터 된 데이터들의 동질성을 보존하도록 클러스터를 추정한다. 언어적인 모델의 효능성은 이전 연구에서 이미 증명되었음에도 불구하고 성능 측면에서 개선시킬 필요성이 있다. 따라서, 본 논문에서는 기존 언어적인 모델의 근사화와 일반화 성능을 모두 향상시키기 위해 언어적인 컨텍스트의 자동적인 생성, 바이어스항의 추가, 결론부 파라미터의 변형된 구조를 통해 이루어진다. 실험결과는 자동차 연료소비량 예측문제와 보스턴 housing 데이터를 통해 제안된 방법이 언어적인 모델뿐만 아니라 기존 방법들보다 우수함을 증명한다.