• 한국언어정보학회지:언어와정보
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• 제5권1호
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• pp.1-15
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• 2001

Floating quantifiers(FQs) in English exhibit both universal and language specific proper- ties This paper discusses how such syntactic and semantic characteristics can be explained in terms of a constraint-based, lexical approach to the floating quanti- fer construction within the framework of Head-Driven Phrase Structure Grammar(HPSG). Based on the assumption and FQs are base-generated VP modifiers, this paper proposes and account in which the semantic contribution of FQs consists of a "lexically retrieved" universal quantifier taking scope over the VP meaning.P meaning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권10호
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• pp.1879-1890
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• 2011

Previous studies of syllable frequency effects have proposed that inhibitory effects due to high first syllable frequency were the products of competitions between activated lexical candidates within a lexical level. However, these studies have primarily used lexical decision tasks to examine the nature of syllable frequency effects. This study investigates whether a syllable frequency effect can arise in semantic categorization tasks and whether phonologically or orthographically defined syllables interact with semantically related variables such as morphological family size. If the syllable frequency effect was created by activations and competitions on a lexical level, it is highly possible that the effect was related to semantic categorization tasks. To test this hypothesis, we conducted two experiments. In Experiment 1, morphological family size and phonological syllable frequency were factorially manipulated. In Experiment 2, morphological family size and orthographic syllable frequency were factorially manipulated. The results demonstrate that morphemes have no relationship with phonological syllables but do with orthographic syllables. This suggests that phonological syllables and orthographic syllables have different roles in the syllable frequency effect on visual word recognition process.

• Journal of Information Processing Systems
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• 제19권5호
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• pp.631-640
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• 2023

Short-text similarity calculation is one of the hot issues in natural language processing research. The conventional keyword-overlap similarity algorithms merely consider the lexical item information and neglect the effect of the word order. And some of its optimized algorithms combine the word order, but the weights are hard to be determined. In the paper, viewing the keyword-overlap similarity algorithm, the short English text similarity algorithm based on lexical chunk theory (LC-SETSA) is proposed, which introduces the lexical chunk theory existing in cognitive psychology category into the short English text similarity calculation for the first time. The lexical chunks are applied to segment short English texts, and the segmentation results demonstrate the semantic connotation and the fixed word order of the lexical chunks, and then the overlap similarity of the lexical chunks is calculated accordingly. Finally, the comparative experiments are carried out, and the experimental results prove that the proposed algorithm of the paper is feasible, stable, and effective to a large extent.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2002년도 봄 학술발표논문집 Vol.29 No.1 (B)
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• pp.238-240
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• 2002

If there is an Information Retrieval system which comprehends the semantic content of documents and knows the preference of users. the system can search the information better on the Internet, or improve the IR performance. Therefore we propose the IR model which combines semantic based indexing and fuzzy relevance model. In addition to the statistical approach, we chose the semantic approach in indexing, lexical chains, because we assume it would improve the performance of the index term extraction. Furthermore, we combined the semantic based indexing with the fuzzy model, which finds out the exact relevance of the user preference and index terms. The proposed system works as follows: First, the presented system indexes documents by the efficient index term extraction method using lexical chains. And then, if a user tends to retrieve the information from the indexed document collection, the extended IR model calculates and ranks the relevance of user query. user preference and index terms by some metrics. When we experimented each module, semantic based indexing and extended fuzzy model. it gave noticeable results. The combination of these modules is expected to improve the information retrieval performance.

• 정보처리학회논문지B
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• 제19B권1호
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• pp.63-76
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• 2012

본 논문에서는 통계기반의 복합명사 분해 방법과 어휘의미망(U-WIN)과 사전 뜻풀이에서 추출한 의미관계 정보를 이용하는 한국어 복합명사 의미 태깅 시스템을 제안한다. 본 시스템은 크게 복합명사 분해, 의미제약, 그리고 의미 태깅의 세 가지 부분으로 이루어진다. 분해과정은 세종말뭉치에서 추출한 위치별명사 빈도를 사용하여 최적의 구성 명사 분해 후보를 선정하고 의미제약을 위한 구성 명사 재분해와 외래어 복원의 과정을 수행한다. 의미범위 제약과정은 유사도 비교의 계산량을 줄이고 정확도를 높이기 위해 원어 정보와 Naive Bayes Classifier를 이용해 가능한 경우 구성 명사의 의미를 선 제약한다. 의미 분석 및 태깅 과정에서는 bigram 구성 명사의 각 의미 유사도를 구하고 하나의 체인을 만들어가며 태깅을 수행한다. 본 시스템의 성능 평가를 위해 표준국어대사전에서 추출한 3음절 이상의 40,717개의 복합명사를 대상으로 의미 태깅된 테스트 셋을 구축하였다. 이를 이용한 실험에서 99.26%의 분해 정확도를 보였으며, 95.38%의 의미 분석 정확도를 보였다.

• 한국지능시스템학회논문지
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• 제22권1호
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• pp.42-48
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• 2012

시맨틱 웹이 대두되면서 온톨로지의 사용이 점차 늘어나고 있다. 동일한 분야에 관한 온톨로지일지라도 구축 방법과 활용 형태에 따라 같은 개념이 다른 형태로 표현되거나, 다른 개념이 같은 형태로 표현될 수 있다. 이러한 온톨로지들을 공유하고 재사용하기 위해서는 온톨로지의 매핑이 필요하다. 본 논문에서는 온톨로지의 어휘 정보를 이용하여 다단계로 매핑하고, 이 결과를 기반으로 구조 정보의 유사성을 검사하는 방법을 제안한다. 온톨로지에서 어휘 정보가 부여되지 않는 블랭크 노드를 추가로 확장하여 매핑 성능을 향상시켰다. 실험을 통하여 86.38%의 F1-measure값을 얻을 수 있었다.

• 디지털산업정보학회논문지
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• 제17권4호
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• pp.103-108
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• 2021

Knowledge graph is a technology that improves search results by using semantic information based on various resources. Therefore, due to these advantages, the knowledge graph is being defined as one of the core research technologies to provide AI-based services recently. However, in the case of the knowledge graph, since the form of knowledge collected from various service domains is defined as plain text, it is very important to be able to analyze the text and understand its meaning. Recently, various lexical dictionaries have been proposed together with the knowledge graph, but since most lexical dictionaries are defined in a language other than Korean, there is a problem in that the corresponding language dictionary cannot be used when providing a Korean knowledge service. To solve this problem, this paper proposes an ontology based on the parts of speech of Korean. The proposed ontology uses 9 parts of speech in Korean to enable the interpretation of words and their semantic meaning through a semantic connection between word class and word class. We also studied various scenarios to apply the proposed ontology to vehicle services.

• International Journal of Contents
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• 제7권4호
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• pp.108-115
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• 2011

The current study aims to analyze the acceptance of International Standard Terminology (IST) related to herbs and formulas used in Korea. It also intends to examine limitations of each term source by linking texts for herbal medicine research and formula research used in schools of oriental medicine with medicinal substance-formula classification names within the IST framework. This study examined 64 medicinal classification names of IST, including synonyms, 41 formula classification names, 65 classification names of "Herbal Medicine Study," 89 medicinal classification names of "Shin's Clinical Herbal Medicine Study," and lastly 83 formula classification names of "Formula Study." Data on their chief virtue, efficacy and characteristics as medicinal substances were extracted from their definitions, and such data were used to perform Chinese character-English mapping using the IST. The outcomes of the mapping were then analyzed in terms of both lexical matching and semantic matching. In terms of classification names for medicinal substances, "Herbal Medicine Study" had 60.0% lexical matching, whereas "Shin's Clinical Herbal Medicine Study" had 48.3% lexical matching. When semantic matching was also applied, "Herbal Medicine Study" showed a value of 87.7% and "Shin's Clinical Herbal Medicine Study" 74.2%. In terms of formula classification names, lexical matching was 28.9% of 83 subjects, and when semantic matching was also considered, the value was 30.1%. When the conceptual elements of this study were applied, some IST terms that are classified with other codes were found to be conceptually consistent, and some terms were not accepted due to different depths in the classification systems of each source.

• 한국문헌정보학회지
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• 제40권3호
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• pp.175-190
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• 2006

최근 수년간 한국어를 위한 어휘의미망에 대한 관심은 꾸준히 높아지고 있지만. 그 결과물을 어떻게 평가하고 활용할 것인가에 대한 방안은 이루어지지 않고 있다. 본 논문에서는 단어클러스터링 시스템 개발을 통하여, 어휘의미망에 의해 확장되기 전후의 클러스터링을 수행하여 데이터를 서로 비교하였다 단어클러스터링 시스템 개발을 위해 사용된 학습 데이터는 신문 말뭉치 기사로 총 68.455.856 어절 규모이며, 특성벡터와 벡터공간모델을 이용하여 시스템A를 완성하였다. 시스템B는 구축된 '(-하)동사류' 3,656개의 어휘의미를 포함하는 동사 어휘의미망을 활용하여 확장된 것으로 확장대상정보를 선택하여 특성벡터를 재구성한다. 대상이 되는 실험 데이터는 '다국어 어휘의미망-코어넷'으로 클러스터링 결과 나타난 어휘의 세 번째 층위까지의 노드 동일성 석부로 정확률을 검수하였다. 같은 환경에서 시스템A와 시스템B를 비교한 결과 단어클러스터링의 정확률이 45.3%에서 46.6%로의 향상을 보였다. 향후 연구는 어휘의미망을 활용하여 좀 더 다양한 시스템에 체계적이고 폭넓은 평가를 통해 전산시스템의 향상은 물론. 연구되고 있는 많은 어휘의미망에 의미 있는 평가 방안을 확대시켜 나가야 할 것이다.

• 한국언어정보학회:학술대회논문집
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• 한국언어정보학회 1996년도 Language, Information and Computation = Selected Papers from the 11th Pacific Asia Conference on Language, Information and Computation, Seoul
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• pp.425-438
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• 1996