• Proceedings of the Korean Society for Cognitive Science Conference
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• 2006.06a
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• pp.1-7
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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 동사어휘의미망을 포함하여 확장된 것으로 확장대상정보를 선택하여 특성벡터를 재구성한다. 대상이 되는 실험 데이터는 '다국어 어휘의미망-코어넷'으로 클러스터링 결과 나타난 어휘들의 세 번째 층위까지의 노드 동일성 여부로 정확률 검수를 하였다. 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.

• Korean Journal of Oriental Medicine
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• v.12 no.3 s.18
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• pp.1-15
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• 2006

For the recent several years, there has been much interest in lexical semantic network. However, it seems to be very difficult to evaluate the effectiveness and correctness of it and invent the methods for applying it into various problem domains. In order to offer the fundamental ideas about how to evaluate and utilize lexical semantic networks, we developed two automatic word clustering systems, which are called system A and system B respectively. 68,455,856 words were used to learn both systems. We compared the clustering results of system A to those of system B which is extended by the lexical-semantic network. The system B is extended by reconstructing the feature vectors which are used the elements of the lexical-semantic network of 3,656 '-ha' verbs. The target data is the 'multilingual Word Net-CoreNet'.When we compared the accuracy of the system A and system B, we found that system B showed the accuracy of 46.6% which is better than that of system A, 45.3%.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.3
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• pp.175-190
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• 2006

For the recent several years, there has been much interest in lexical semantic network However it seems to be very difficult to evaluate the effectiveness and correctness of it and invent the methods for applying it into various problem domains. In order to offer the fundamental ideas about how to evaluate and utilize lexical semantic networks, we developed two automatic vol·d clustering systems, which are called system A and system B respectively. 68.455.856 words were used to learn both systems. We compared the clustering results of system A to those of system B which is extended by the lexical-semantic network. The system B is extended by reconstructing the feature vectors which are used the elements of the lexical-semantic network of 3.656 '-ha' verbs. The target data is the 'multilingual Word Net-CoroNet'. When we compared the accuracy of the system A and system B, we found that system B showed the accuracy of 46.6% which is better than that of system A. 45.3%.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.339-344
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• 2012

Established continuous vocabulary recognition system improved recognition rate by using decision tree based tying modeling method. However, since system model cannot support the retrieve of phoneme data, it is hard to secure the accuracy. In order to improve this problem, we remodeled a system that could retrieve probabilistic model from continuous vocabulary clustering model to phoneme unit. Therefore in this paper showed 95.88%of recognition rate in system performance.

• The Journal of Korean Association of Computer Education
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• v.13 no.1
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• pp.65-73
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• 2010

This study offered lexical knowledge acquisition model of unsupervised learning method in order to overcome limitation of lexical knowledge hand building manual of supervised learning method for research of natural language processing. The offered model obtains the lexical knowledge from the lexical entry which was given by inputting through the process of vectorization, clustering, lexical knowledge acquisition automatically. In the process of obtaining the lexical knowledge acquisition of model, some parts of lexical knowledge dictionary which changes in the number of lexical knowledge and characteristics of lexical knowledge appeared by parameter changes were shown. The experimental results show that is possibility of automatic building of Machine-readable dictionary, because observed to the number of lexical class information cluster collected constant. also building of lexical ditionary including left-morphosyntactic information and right-morphosyntactic information is reflected korean characteristic.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.463-465
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• 2002

자동 문서 요약 시스템은 문서내 담겨있는 정보를 최대만 표현하면서 문서의 크기를 줄이는 시스템이다. 본 논문에서는 어휘를 자동으로 클러스터링하여 문서 대표어를 찾고, 이를 제목과 조합하여 요약을 수행하는 시스템을 제안한다. 특히 이 시스템은 제목이 없는 문서도 요약을 수행할 수 있는 장점이 있다. 비교시스템으로는 제목, 위치, 빈도를 이용만 시스템을 구축하여 사용하였으며 30%, 10%, 그리고 4문장 요약에서 제안한 시스템은 모두 우수한 성능을 보였다.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.177-183
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• 2010

In continuous vocabulary recognition system by statistical method vocabulary recognition to be performed using probability distribution it also modeling using phoneme clustering for based sample probability parameter presume. When vocabulary search that low recognition rate problem happened in express vocabulary result from presumed probability parameter by not defined phoneme and insert phoneme and it has it's bad points of gaussian model the accuracy unsecure for one clustering modeling. To improve suggested probability distribution mixed gaussian model to optimized for based resemble Euclidean and Bhattacharyya distance measurement method mixed clustering modeling that system modeling for be searching phoneme probability model in clustered model. System performance as a result of represent vocabulary dependence recognition rate of 98.63%, vocabulary independence recognition rate of 97.91%.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.35-39
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• 2020

DNN error is small compared to the conventional speech recognition system, DNN is difficult to parallel training, often the amount of calculations, and requires a large amount of data obtained. In this paper, we generate a phoneme unit to estimate the GMM parameters with each phoneme model parameters from the GMM to solve the problem efficiently. And it suggests ways to improve performance through clustering for a specific vocabulary to effectively apply them. To this end, using three types of word speech database was to have a DB build vocabulary model, the noise processing to extract feature with Warner filters were used in the speech recognition experiments. Results using the proposed method showed a 97.9% recognition rate in speech recognition. In this paper, additional studies are needed to improve the problems of improved over fitting.

• Annual Conference on Human and Language Technology
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• 2010.10a
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• pp.46-51
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• 2010

한국어의 어휘의미 정보를 명확히 파악하기 위해서는 어휘 의미 체계를 구축해야 한다. 본 논문에서는 어휘 의미 체계 구축의 단계 중 하나인 용언의 의미 군집화를 연구하였다. 주어 및 목적어의 논항 구조와 선택 제약정보, 부사의 결합정보를 이용한 이전의 연구와는 달리 의미태깅이 된 사전 뜻풀이의 용언정보를 이용하여 용언의 의미 군집화와 간단한 계층화를 시도하였다. 그리고 특정 부류의 일반 샘플을 이용했던 특정 용언의 부류가 아닌 사전에 존재하는 대부분의 용언들을 대상으로 연구를 진행하였다.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.399-404
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• 2010

In this paper, we propose a method for precision improvement based on core clusters and term proximity. The method is composed by three steps. The initial retrieval documents are clustered based on query term combination, which occurred in the document. Core clusters are selected by using proximity between query terms. Then, the documents in core clusters are reranked based on context information of query. On TREC AP test collection, experimental results in precision at the top documents(P@100) show that the proposed method improved 11.2% over the language model.