• The KIPS Transactions:PartB
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• 제11B권6호
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• pp.749-758
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• 2004

In this paper, we propose a new method utilizing only raw corpus without additional human effort for disambiguation of target word selection in English-Korean machine translation. We use two data-driven techniques; one is the Latent Semantic Analysis(LSA) and the other the Probabilistic Latent Semantic Analysis(PLSA). These two techniques can represent complex semantic structures in given contexts like text passages. We construct linguistic semantic knowledge by using the two techniques and use the knowledge for target word selection in English-Korean machine translation. For target word selection, we utilize a grammatical relationship stored in a dictionary. We use k- nearest neighbor learning algorithm for the resolution of data sparseness Problem in target word selection and estimate the distance between instances based on these models. In experiments, we use TREC data of AP news for construction of latent semantic space and Wail Street Journal corpus for evaluation of target word selection. Through the Latent Semantic Analysis methods, the accuracy of target word selection has improved over 10% and PLSA has showed better accuracy than LSA method. finally we have showed the relatedness between the accuracy and two important factors ; one is dimensionality of latent space and k value of k-NT learning by using correlation calculation.

• The KIPS Transactions:PartB
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• 제8B권6호
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• pp.685-692
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• 2001

This paper describes how to disambiguate the aspectual meaning of Japanese expression "-te iru" in Japanese-Korean machine translation Due to grammatical similarities of both languages, almost all Japanese- Korean MT systems have been developed under the direct MT strategy, in which the lexical disambiguation is essential to high-quality translation. Japanese has a progressive aspectual marker “-te iru" which is difficult to translate into Korean equivalents because in Korean there are two different progressive aspectual markers: "-ko issta" for "action progressive" and "-e issta" for "state progressive". Moreover, the aspectual system of both languages does not quite coincide with each other, so the Korean progressive aspect could not be determined by Japanese meaning of " te iru" alone. The progressive aspectural meaning may be parially determined by the meaning of predicates and also the semantic meaning of predicates may be partially reshicted by adverbials, so all Japanese predicates are classified into five classes : the 1nd verb is used only for "action progrssive",2nd verb generally for "action progressive" but occasionally for "state progressive", the 3rd verb only for "state progressive", the 4th verb generally for "state progressive", but occasIonally for "action progressive", and the 5th verb for the others. Some heuristic rules are defined for disambiguation of the 2nd and 4th verbs on the basis of adverbs and abverbial phrases. In an experimental evaluation using more than 15,000 sentances from "Asahi newspapers", the proposed method improved the translation quality by about 5%, which proves that it is effective in disambiguating "-te iru" for Japanese-Korean machine translation.translation quality by about 5%, which proves that it is effective in disambiguating "-te iru" for Japanese-Korean machine translation.anslation.

• Journal of KIISE:Software and Applications
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• 제27권7호
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• pp.769-783
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• 2000

In this paper, in order to disambiguate Korean noun word sense, we define a local context and explain how to extract it from a raw corpus. Following the intuition that two different nouns are likely to have similar meanings if they occur in the same local context, we use, as a clue, the word that occurs in the same local context where the target noun occurs. This method increases the usability of extracted knowledge and makes it possible to disambiguate the sense of infrequent words. And we can overcome the data sparseness problem by extending the verbs in a local context. The sense of a target noun is decided by the maximum similarity to the clues learned previously. The similarity between two words is computed by their concept distance in the sense hierarchy borrowed from WordNet. By reducing the multiplicity of clues gradually in the process of computing maximum similarity, we can speed up for next time calculation. When a target noun has more than two local contexts, we assign a weight according to the type of each local context to implement the differences according to the strength of semantic restriction of local contexts. As another knowledge source, we get a co-occurrence information from dictionary definitions and example sentences about the target noun. This is used to support local contexts and helps to select the most appropriate sense of the target noun. Through experiments using the proposed method, we discovered that the applicability of local contexts is very high and the co-occurrence information can supplement the local context for the precision. In spite of the high multiplicity of the target nouns used in our experiments, we can achieve higher performance (89.8%) than the supervised methods which use a sense-tagged corpus.

• KIPS Transactions on Software and Data Engineering
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• 제3권6호
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• pp.219-230
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• 2014

An analysis of dependency relation is a job that determines the governor and the dependent between words in sentence. The dependency relation of predicate is established by patterns and selectional restriction of subcategorization of the predicate. This paper proposes a method of analysis of Korean dependency relation using homograph predicate disambiguated in morphology analysis phase. The disambiguated homograph predicates has each different pattern. Especially reusing a stage transition training dictionary used during tagging POS and homograph, we propose a method of fixing the dependency relation of {noun+postposition, predicate}, and we analyze the accuracy and an effect of homograph for analysis of dependency relation. We used the Sejong Phrase Structured Corpus for experiment. We transformed the phrase structured corpus to dependency relation structure and tagged homograph. From the experiment, the accuracy of dependency relation by disambiguating homograph is 80.38%, the accuracy is increased by 0.42% compared with one of undisambiguated homograph. The Z-values in statistical hypothesis testing with significance level 1% is ${\mid}Z{\mid}=4.63{\geq}z_{0.01}=2.33$. So we can conclude that the homograph affects on analysis of dependency relation, and the stage transition training dictionary used in tagging POS and homograph affects 7.14% on the accuracy of dependency relation.

• Annual Conference on Human and Language Technology
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• 한국정보과학회언어공학연구회 2003년도 제15회 한글 및 한국어 정보처리 학술대회
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• pp.246-251
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• 2003

한국어의 언어적 특성에 의해 빈번하게 등장하는 명사와 기능어의 나열은 기능어나 연결 구문의 잦은 생략현상에 의해 복합 명사의 출현을 발생시킨다. 따라서, 한국어 분석에서 복합 명사의 처리 방법은 매우 중요한 문제로 인식되었으며 활발한 연구가 진행되어 왔다. 복합 명사의 의미 결정은 복합 명사구 내 단위 명사간의 의미적인 수식 관계를 고려하여 머리어의 선택과 의미를 함께 결정할 필요가 있다. 본 논문에서는 정보 검색의 색인어 추출 방법에서 사용되는 복합 명사구 내의 서술성 명사 처리를 이용하여 복합 명사의 의미 결정을 인접 명사의 의미 공기 정보가 아닌 구문관계에 따른 의미 공기 정보를 사용하여 분석하는 방법을 제시한다. 복합 명사구 내에서 구문적인 관계는 명사구 내에 서술성 명사가 등장하는 경우 보-술 관계에 의한 격 결정 문제로 전환할 수 있다. 이러한 구문 구조는 명사 의미를 결정할 수 있는 추가적인 정보로 활용할 수 있으며, 이때 구문 구조 파악을 위해 구축된 의미 제약 조건을 활용하도록 한다. 구조 분석에서 사용되는 격틀 정보는 동사와 공기하는 명사의 구문 관계를 분석하기 위해 의미 정보를 제약조건으로 하여 구축된다. 이러한 의미 격틀 정보는 단문 내 명사들의 격 결정과 격을 채우는 명사 의미를 결정할 수 있는 정보로 활용된다. 본 논문에서는 현재 개발중인 한영 기계 번역 시스템 Tellus-KE의 단문 단위 대역어 선정을 위해 구축된 의미 대역패턴인 동사구 패턴을 사용한다. 동사구 패턴에 기술된 한국어의 단문 단위 의미 격 정보를 사용하는 경우, 격결정을 위해 사용되는 의미 제약 조건이 복합 명사의 중심어 선택과 의미 결정에 재활용 될 수 있으며, 병렬말뭉치에 의해 반자동으로 구축되는 의미 대역 패턴을 사용하여 데이터 구축의 어려움을 개선하고자 한다. 및 산출 과정에 즉각적으로 활용될 수 있을 것이다. 또한, 이러한 정보들은 현재 구축중인 세종 전자사전에도 직접 반영되고 있다.teness)은 언화행위가 성공적이라는 것이다.[J. Searle] (7) 수로 쓰인 것(상수)(象數)과 시로 쓰인 것(의리)(義理)이 하나인 것은 그 나타난 것과 나타나지 않은 것들 사이에 어떠한 들도 없음을 말한다. [(성중영)(成中英)] (8) 공통의 규범의 공통성 속에 규범적인 측면이 벌써 있다. 공통성에서 개인적이 아닌 공적인 규범으로의 전이는 규범, 가치, 규칙, 과정, 제도로의 전이라고 본다. [C. Morrison] (9) 우리의 언어사용에 신비적인 요소를 부인할 수가 없다. 넓은 의미의 발화의미(utterance meaning) 속에 신비적인 요소나 애정표시도 수용된다. 의미분석은 지금 한글을 연구하고, 그 결과에 의존하여서 우리의 실제의 생활에 사용하는 $\ulcorner$한국어사전$\lrcorner$ 등을 만드는 과정에서, 어떤 의미에서 실험되었다고 말할 수가 있는 언어과학의 연구의 결과에 의존하여서 수행되는 철학적인 작업이다. 여기에서는 하나의 철학적인 연구의 시작으로 받아들여지는 이 의미분석의 문제를 반성하여 본다.반인과 다르다는 것이 밝혀졌다. 이 결과가 옳다면 한국의 심성 어휘집은 어절 문맥에 따라서 어간이나 어근 또는 활용형 그 자체로 이루어져 있을 것이다.으며, 레드 클로버 + 혼파 초지가 건물수량과 사료가치를 높이는데 효과적이었다.\ell}$ 이었으며 , yeast extract 첨가(添加)하여 배양시(培養時)는 yeast extract 농도(濃度)가 증가(增加)함에 따라 단백질(蛋白質) 함량(含量)도 증가(增加)하였다. 7. CHS-13 균주(菌株)의 RNA 함량(

• Journal of Technology Innovation
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• 제32권2호
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• pp.23-58
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• 2024

Despite the important roles of institutions and their collaboration in producing knowledge for innovation, the lack of accurate methods for identifying such knowledge-producing institutions has restricted empirical research on the role of institutions in innovation. This study explores methods to enhance the accuracy of identifying institutions involved in innovation process. To this end, we propose ways to improve accuracy in both aspects of information - data and algorithms - using bibliographic information in the digital health field. Specifically, in the data processing stage before applying algorithms, we address contextual inaccuracies of bibliographic information; in the algorithm application stage, we propose methods to improve the ambiguity of institution names (IND). When compared with the PKG dataset, which is publicly available datasets based on the same bibliographic information, our methods doubled the number of cases available for subsequent analysis. We also discovered that the contribution of Korean institutions in the digital health field is either underestimated or overestimated. The method presented in this study is expected to contribute to empirically researching the role of knowledge-producing institutions in innovation process and ecosystem.