• Proceedings of the Korean Society for Cognitive Science Conference
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• 2002.05a
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• pp.211-215
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• 2002

본 실험은 종합적 인지과정을 추론할 수 있는 결합 문제(binding problem)를 언어적인지 과정을 통해 알아 본 실험으로, 총 10 명(남:61여:4, 평균나이:24.40 $\pm$ 1.35)의 정상군을 대상으로, 4개의 음소로 이루어진 단음절 명사를 목표 자극(target stimulus)으로 하고, 4개 음소의 임의적인 조합으로서 글자를 이루지 못하는 비목표 자극(non-target stimulus)을, 각각 200 회와 800 회씩 시각적으로 0.5초씩 무작위로 제시하여 128 채널 고밀도 사건관련전위(ERP)를 측정하였다. 이번 실험 결과의 주요 특징은 글자가 아닌 비목표 자극보다 글자인 목표 자극에서 두드러지게 나타난 두정엽 부근의 P500 과 N900 이라고 할 수 있다. 자극 제시 비율의 차이에서 오는 oddball 효과로 인한 기존 P300 의 인지적 의미를 이번 결과의 P500 이 함축한다고 볼 수 있으며, 단음절 단어를 인지할 때, 글자임을 인식하는 순간은 의미적인지 과정이 진행되었다기보다 그 글자의 형태만으로 낯익은 글자인지를 분간하는 것으로 보인다 따라서, 이 경우 기존 언어 실험에 자주 등장하던 의미론적 peak 인 N400 은 보이지 않고, 곧바로 형태적이고, 통사적(syntactic)인 인지 처리 과정인 P500이 나타났다고 해석할 수 있다. 하지만, 이번 실험에서는 N400 대신에 N900 이 나타났다. 이 결과는 이번 ERP 실험과 병행된 프로토콜 분석을 통해, 피험자가 자극 제시 후, 약 900ms 정도에, 이미 제시되고 사라진 글자 자극을 다시 한번 떠올리는 인지 과정이 일어난다는 점과 관련 지어 해석하면, 기존에 의미적(semantic) 인지 과정으로만 해석했던 negative-peak 를 생각(thinking)과 같은 내재적인지 과정(internal cognitive process)으로 확장하여 일반화하는 추론도 생각해 볼 수 있다. 요컨대, 언어인지를 통한 이번 실험을 통해, 뇌파에서 검출되는 negative-peak 은 internal cognitive process로 추측되고, positive-peak 는 external cognitive process 라고 생각된다. 덧붙여, 유의해서 볼 점은 각 peak-topology 에서 Cz 의 진폭이 Fz 보다 크게 나온 점과, 일반적으로 언어 기능을 담당한다는 좌측 측두엽(T7)이 우측(T8)보다 통계적으로 더 유의미한 차이를 보였다는 점등이다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.1707-1714
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• 2015

The neural bases underlying within or between-language picture naming was investigated by using event-related fMRI. The present suudy explorered the following two goals: The first is to compare cortical activation areas relevant to naming process in native and foreign language, and to decide whether the activation pattern of the foreign word will be the same as native words or not. The next is to find the cerebral areas involved only in alternating language switching between native and foreign language condition. Differential activation patterns were observed for language switching against one-language. Both naming tasks all activated the left inferior frontal gyrus (LIFG) as expected. However the differences in naming between languages were reflected in the activation amount of the LIFG, namely more activation in naming the native language than the foreign language. Especially, naming of the foreign word from English showed the similar area and size in activation with native language suggesting that the process of borrowed noun resembles that of native common noun. And the language switching between languages newly activated the right middle frontal gyrus as well as the left inferior frontal areas. The right middle frontal gyrus engagement in switching conditions obviously identified that right hemisphere is recruited in code switching possibly with respect to meta-cognition controlling language index at a subconscious level.

• Cross-Cultural Studies
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• v.53
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• pp.131-161
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• 2018

Emotional expressions are expressions that show the internal condition of mind or consciousness. Types of emotional expressions include vocabulary that describes emotion, the composition of sentences that expresses emotion such as an exclamatory sentence and rhetorical question, expressions of interjection, appellation, causative, passive, adverbs of attitude for an idea, and a style of writing. This study focuses on vocabulary that describes emotion and analyzes the aspect of translation when emotional expressions of 'Hu(怖)' is shown on "Kokoro". The aspect of translation was analyzed by three categories as follows; a part of speech, handling of subjects, and classification of meanings. As a result, the aspect of translation for expressions of Hu(怖)' showed that they were translated to vocabulary as they were suggested in the dictionary in some cases. However, they were not always translated as they were suggested in the dictionary. Vocabulary that described the emotion of 'Hu(怖)' in Japanese sentences were mostly translated to their corresponding parts of speech in Korean. Some adverbs needed to add 'verbs' when they were translated. Also, different vocabulary was added or used to maximize emotion. However, the correspondence of a part of speech in English was different from Korean. Examples of Japanese sentences that expressed 'Hu(怖)' by verbs were translated to expression of participles for passive verbs such as 'fear', 'dread', 'worry', and 'terrify' in many cases. Also, idioms were translated with focus on the function of sentences rather than the form of sentences. Examples, what was expressed in adverbs did not accompany verbs of 'Hu (怖)'. Instead, it was translated to the expression of participles for passive verbs and adjectives such as 'dread', 'worry', and 'terrify' in many cases. The main agents of emotion were shown in the first person and the third person in simple sentences. The translation on emotional expressions when a main agent was the first person showed that the fundamental word order of Japanese was translated as it was in Korean. However, adverbs of time and adverbs of degree tended to be added. Also, the first person as the main agent of emotion was positioned at the place of subject when it was translated in English. However, things or the cause of events were positioned at the place of subject in some cases to show the degree of 'Hu(怖)' which the main agent experienced. The expression of conjecture and supposition or a certain visual and auditory basis was added to translate the expression of emotion when the main agent of emotion was the third person. Simple sentences without a main agent of emotion showed that their subjects could be omitted even if they were essential components because they could be known through context in Korean. These omitted subjects were found and translated in English. Those subjects were not necessarily humans who were the main agents of emotion. They could be things or causes of events that specified the expression of emotion.

• Cross-Cultural Studies
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• v.51
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• pp.329-356
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• 2018

Types of emotional expressions are comprised of vocabulary that describes emotion and composition of sentences to express emotion such as an exclamatory sentence and a rhetorical question, expressions of interjection, adverbs of attitude for an idea, and a style of writing. This study is focused on vocabulary that describes emotion and analyzes the aspect of translation when emotional expression of 'Kyo(驚)' is shown in "Kokoro". As a result, the aspect of translation for expression of 'Kyo(驚)' showed that it was translated to vocabulary as suggested in the dictionary in some cases. However, it was not always translated as suggested in the dictionary. Vocabulary that describes the emotion of 'Kyo(驚)' in Japanese sentences is mostly translated to corresponding parts of speech in Korean. Some adverbs needed to add 'verbs' when they were translated. Different vocabulary was added or used to maximize emotion. However, the corresponding part of speech in English was different from Korean. Examples of Japanese sentences expressing 'Kyo(驚)' by verbs were translated to expression of participles for passive verbs such as 'surprise' 'astonish' 'amaze' 'shock' 'frighten' 'stun' in many cases. Idioms were also translated with focus on the function of sentences rather than the form of sentences. Those expressed in adverbs did not accompany verbs of 'Kyo(驚)'. They were translated to expression of participles for passive verbs and adjectives such as 'surprise' 'astonish' 'amaze' 'shock' 'frighten' 'stun' in many cases. Main agents of emotion were showat the first person and the third person in simple sentences. Translation of emotional expressions when a main agent was the first person showed that the fundamental word order of Japanese was translated as in Korean. However, adverbs of time and adverbs of degree were ended to be added. The first person as the main agent of emotion was positioned at the place of subject when it was translated in English. However, things or causes of events were positioned at the place of subject in some cases to show the degree of 'Kyo(驚)' which the main agent experienced. The expression of conjecture and supposition or a certain visual and auditory basis was added to translate the expression of emotion when the main agent of emotion was the third person. Simple sentences without the main agent of emotion showed that their subjects could be omitted even if they were essential components because they could be known through context in Korean. These omitted subjects were found and translated in English. Those subjects were not necessarily human who was the main agent of emotion. They could be things or causes of events that specified the expression of emotion.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.