• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.423-428
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• 2017

These days, various and tons of education information are rapidly increasing and spreading due to Internet and smart devices usage. Recently, as e-Learning usage increasing, many instructors and students (learners) need to set a goal to maximize learners' result of education and education system efficiency based on big data analytics via online recorded education historical data. In this paper, the author applied Word2Vec algorithm (neural network algorithm) to find similarity among education words and classification by clustering algorithm in order to objectively recognize and analyze online recorded education historical data. When the author applied the Word2Vec algorithm to education words, related-meaning words can be found, classified and get a similar vector values via learning repetition. In addition, through experimental results, the author proved the part of speech (noun, verb, adjective and adverb) have same shortest distance from the centroid by using clustering algorithm.

• Science of Emotion and Sensibility
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• v.12 no.4
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• pp.433-442
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• 2009

People are tending to buy products through the Internet rather than purchasing them from the store. Some of the consumers give their feedback on line such as reviews, replies, comments, and blogs after they purchased the products. People are also likely to get some information through the Internet. Therefore, companies and public institutes have been facing this situation where they need to collect and analyze reviews or public opinions for them because many consumers are interested in other's opinions when they are about to make a purchase. However, most of the people's reviews on web site are too numerous, short and redundant. Under these circumstances, the emotion scanning system of text documents on the web is rising to the surface. Extracting writer's opinions or subjective ideas from text exists labeled words like GI(General Inquirer) and LKB(Lexical Knowledge base of near synonym difference) in English, however Korean language is not provided yet. In this paper, we labeled positive, negative, and neutral attribute at 4 POS(part of speech) which are noun, adjective, verb, and adverb in Korean dictionary. We extract construction patterns of emotional words and relationships among words in sentences from a large training set, and learned them. Based on this knowledge, comments and reviews regarding products are classified into two classes polarities with positive and negative using SO-PMI, which found the optimal condition from a combination of 4 POS. Lastly, in the design of the system, a flexible user interface is designed to add or edit the emotional words, the construction patterns related to emotions, and relationships among the words.

• Korean Journal of Cognitive Science
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• v.20 no.4
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• pp.507-533
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• 2009

This paper examines the time course and processing patterns of filler-gap dependencies in Korean relative clauses, using an eyetracking method. Participants listened to a short story while viewing four pictures of entities mentioned in the story. Each story is followed by an auditorily presented question involving a relative clause (subject relative or dative relative). Participants' eye movements in response to the question were recorded. Results showed that the proportion of looks to the picture corresponding to a filler noun significantly increased at the relative verb affixed with a relativizer, and was largest at the filler where the fixation duration on the filler picture significantly increased. These results suggest that online resolution of the filler-gap dependency only starts at the relative verb marked with a relativiser and is finally completed at the filler position. Accordingly, they partly support the filler-driven parsing strategy for Korean, as for head-initial languages. In addition, the different patterns of eye movements between subject relatives and dative relatives indicate the role of case markers in parsing Korean sentences.

• Weed & Turfgrass Science
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• v.3 no.2
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• pp.78-85
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• 2014

The survey of weed occurrence was conducted to identify dominant weed species in the paddy field. Total 346 sites of the 12 regions in Chungbuk Province in Korea were investigated in July, 2013. Weed flora was composed of 43 species belonged to 15 families. The compositions of major plant families, Cyperaces, Poaceae, Pontederiaceae and Asteraceae, were 21.5, 17.4, 15.5 and 12.7%, respectively. Based on life cycle, weed species was grouped into annuals of 71.3 and perennials of 28.1%. The most dominant weed species in paddy fields of Chungbuk Province were Monochoria vaginalis (14.5%), followed by Scirpus juncoides (10.5%), Echinochloa oryzoides (9.3%), Eleocharis kuroguwai (7.0%), Aeschynomene indica (6.2%) et al. The similarity of paddy weeds in 12 regions observed through TWINSPAN analysis was distinguished by Persicaria longiseta, Aneilema keisak, Persicaria thunbergii, Fimbristylis millacea, Blyxa japonica, Digitaria ciliaris, Potamogeton distinctus, Cyperus nipponicus. This information could be useful for estimation of future weed occurrence and establishment of weed control methods in chungbuk province in Korea.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.386-391
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• 2015

This paper proposed the optimal attributes for text classification based on Korean and Chinese linguistic features. The experiments committed to discover which is the best feature among n-grams which is known as language independent, morphemes that have language dependency and some other feature sets consisted with n-grams and morphemes showed best results. This paper used SVM classifier and Internet news for text classification. As a result, bi-gram was the best feature in Korean text categorization with the highest F1-Measure of 87.07%, and for Chinese document classification, 'uni-gram+noun+verb+adjective+idiom', which is the combined feature set, showed the best performance with the highest F1-Measure of 82.79%.

• JOURNAL OF ELECTRICAL WORLD
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• s.310
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• pp.62-66
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• 2002

오늘날의 빌딩이나 역사(驛舍)는 쾌적함을 위해 조명설비, 공조설비, 위생설비, 엘리베이터, 에스컬레이터, 방범방재설비, 통신정보설비 등이 잘 정비되어 있으며, 그 기기 모두는 전기로 작동되고 있다. 빌딩이나 교통전원설비의 리뉴얼공사를 하는 데는 어떻게 빌딩과 교통설비의 기능을 정지하지 않고 단기간에 완성하는가가 중요 포인트가 된다. 미쓰비시(삼릉)전기 플랜트건설총괄부에서는 고객니즈에 대한 검토뿐만 아니라 주변지역에 주는 영향도 충분히 조사하여 빌딩이나 철도 역사에 출입하는 사람들의 안전과 일상생활을 배려한 계획을 세워, 그 시공장소에 적합한 공법 개선과 최적의 기자재 공법을 개발하고 추진하여 안전$\cdot$품질을 충분히 고려한 현지시공을 실시하고 있다. (1) 반입공법 빌딩과 교통시설의 전원설비는 고층이나 지하에 설치되는 일이 많고 건물의 성질상, 전용 반입설비가 확보되어 있지 않는 것이 통상적이다. 동사에서는 고소반입용기기 ''뉴톤밸런스''((1))와 계단용반입기기 ''계단승강기''((2))를 개발하여 단(短)공기로 지역주민이나 빌딩 이용자에게 지장을 주지 않는 공법을 채용하여 높은 평가를 받고 있다. (2) 설치$\cdot$조립공법 건물 내의 작업은 환경을 악화시키지 않는 것이 중요하다. 저소음으로 배기가스를 발생시키지 않도록 ''전동크레인''((3))과 ''돔보(잠자리)크레인''((4))을 채용하고 있다. 또 소음$\cdot$진동을 발생시키지 않는 콘크리트용의 앵커구멍 뚫기장치 ''라구라구(樂樂)드릴''((5))과 앵커타설장치 ''라구라구(樂樂)햄머''((6)) 그리고 앵커강도확인장치 ''라구라구(樂樂)체커''((7)) 등의 기기를 개발하여 작업환경을 향상시켰다. $\divideontimes$ 위의 (1)$\~$(7)과 다음 페이지의 그림 중 (1)$\~$(7)은 대응하고 있다.

• JOURNAL OF ELECTRICAL WORLD
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• s.302
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• pp.64-70
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• 2002

현재 미쓰비시(삼릉)전기를 비롯한 빌딩관련업계에서는 빌딩의 재건축이나 설비투자는 감소되고 건물$\cdot$설비의 장(長)수명화가 도모되고 있으며, 전체수명에 걸친 경제적 부담을 종합적으로 억제함으로써 러닝코스트를 최적화하려는 경향은 더욱 높아질 것으로 예측된다. 빌딩의 기획$\cdot$설계단계에서부터 폐기까지의 라이프사이클에서 러닝코스트가 점하는 비율은 라이프사이클 코스트(LCC)전체의 $70\%$에서 $80\%$가 된다. 이것을 억제하는 것은 중요한 과제로서, 그 지원시스템으로 고도의 빌딩매니지먼트 시스템(BMS)이 필요하게 된다. 개발한 BMS는 중$\cdot$대규모 빌딩시스템 확대판매를 위한 새로운 시스템이지만 종합관리서비스를 위한 수탁용 지원시스템이기도 하다. 빌딩 라이프사이클의 전(全)단계(기획$\cdot$설계$\rightarrow$건설$\rightarrow$운용관리$\rightarrow$처분)에서 대응하게 되어 있다. 동사그룹(미쓰비시전기 및 미쓰비시전기 빌딩테크노서비스)은 빌딩오너의 경영적 시각에서, 활동이 가능하며 메이커와 서비스회사의 구분없이 다방면에 걸쳐 역할을 담당하고 있다. 또 고객에게 가치를 제공하는 기술과 전국적인 대응체제는 BMS 개발에 큰 힘이 되고 있다. 개발한 BMS의 특징은, 지금까지의 설비관리시스템의 실적을 베이스(경험, 노하우 등)로 다시 보완하여 장치함으로써 임차인관리시스템, 나아가 에너지 절약 요구에 응한 에너지관리까지 확대하여 유기적(관련정보와 연계를 갖도록 한다는 의미)으로 구축한 시스템을 완성한 점이다.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.43-52
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• 2012

Semantic role labeling is the research area analyzing the semantic relationship between elements in a sentence and it is considered as one of the most important semantic analysis research areas in natural language processing, such as word sense disambiguation. However, due to the lack of the relative linguistic resources, Korean semantic role labeling research has not been sufficiently developed. We, in this paper, propose an automatic predicate-argument analyzer to begin constructing the Korean PropBank which has been widely utilized in the semantic role labeling. The analyzer has mainly two components: the semantic lexical dictionary and the automatic predicate-argument extractor. The dictionary has the case frame information of verbs and the extractor is a module to decide the semantic class of the argument for a specific predicate existing in the syntactically annotated corpus. The analyzer developed in this research will help the construction of Korean PropBank and will finally play a big role in Korean semantic role labeling.

• Annual Conference on Human and Language Technology
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• 2012.10a
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• pp.23-28
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• 2012

번역을 지원하는 도구로 자동 번역 시스템을 효율적으로 활용하기 위해 중요한 것은 자동 번역에 적합하도록 원문을 작성하거나 이미 작성된 원문에 대한 전처리 작업을 하는 것이다. 본 연구의 궁극적인 목표는 제품 설명서 작성자가 통제언어 체커를 통해 통제언어 규칙들을 적용하여 원문을 작성하도록 하는 것이다. 본 논문은 그 중간 단계로써 제품 설명서에 나타나는 문제 사항이 번역 품질에 어떠한 영향을 미치는지 밝혀내는 것을 목적으로 한다. 연구 대상은 제품 설명서에서 자동 번역의 성능을 저해시키는 요소 중 중의적 명사 수식 구문이다. 이러한 명사 수식 구문들은 분석 단계에서 구조적인 모호성을 초래하여 한국어 분석의 정확도를 떨어뜨리기 때문에 결과적으로 번역 품질을 악화시킬 수 있다. 이를 검증하기 위해 우선 제품 설명서 데이터를 분석하여 자동 번역 결과에 부정적인 영향을 미치는 명사 수식 구문을 다음과 같이 4가지로 유형화 하였다. (유형 1) 관형격 명사구 + 명사 병렬 접속, (유형 2) 동사의 관형형이 수식하는 명사구 + 명사 병렬 접속, (유형 3) 관형격 조사 '의' 중복, (유형 4) 병렬 접속어를 잘못 쓴 경우, 각각의 유형에 대해서 한국어 분석 단계에서 발생할 수 있는 문제에 대해 설명하였으며, 문제 사항에 대해 통제언어 규칙을 제시하였다. 통제언어 규칙에 따라 중의적 명사 수식 구문을 수정한 결과, 한국어 원문의 번역결과보다 한국어 수정문의 번역결과가 작성자의 의도를 더 잘 나타낸다는 것을 확인할 수 있었다.

• Language and Information
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• v.14 no.1
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• pp.113-144
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• 2010

The primary goal of this paper is to find a feasible way to answer the question: Does the similarity in meaning between verbs relate to the similarity in their subcategorization? In order to answer this question in a rather concrete way on the basis of a large set of English verbs, this study made use of various language resources, tools, and statistical methodologies. We first compiled a list of 678 verbs that were selected from the most and second most frequent word lists from the Colins Cobuild English Dictionary, which also appeared in WordNet 3.0. We calculated similarity measures between all the pairs of the words based on the 'jcn' algorithm (Jiang and Conrath, 1997) implemented in the WordNet::Similarity module (Pedersen, Patwardhan, and Michelizzi, 2004). The clustering process followed, first building similarity matrices out of the similarity measure values, next drawing dendrograms on the basis of the matricies, then finally getting 177 meaningful clusters (covering 437 verbs) that passed a certain level set by z-score. The subcategorization frames and their frequency values were taken from the ICE-GB. In order to calculate the Selectional Preference Strength (SPS) of the relationship between a verb and its subcategorizations, we relied on the Kullback-Leibler Divergence model (Resnik, 1996). The SPS values of the verbs in the same cluster were compared with each other, which served to give the statistical values that indicate how much the SPS values overlap between the subcategorization frames of the verbs. Our final analysis shows that the degree of overlap, or the relationship between semantic similarity and the subcategorization frames of the verbs in English, is equally spread out from the 'very strongly related' to the 'very weakly related'. Some semantically similar verbs share a lot in terms of their subcategorization frames, and some others indicate an average degree of strength in the relationship, while the others, though still semantically similar, tend to share little in their subcategorization frames.