• Title/Summary/Keyword: 문맥

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Improving Recall for Context-Sensitive Spelling Correction Rules using Conditional Probability Model with Dynamic Window Sizes (동적 윈도우를 갖는 조건부확률 모델을 이용한 한국어 문맥의존 철자오류 교정 규칙의 재현율 향상)

  • Choi, Hyunsoo;Kwon, Hyukchul;Yoon, Aesun
    • Journal of KIISE
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    • v.42 no.5
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    • pp.629-636
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    • 2015
  • The types of errors corrected by a Korean spelling and grammar checker can be classified into isolated-term spelling errors and context-sensitive spelling errors (CSSE). CSSEs are difficult to detect and to correct, since they are correct words when examined alone. Thus, they can be corrected only by considering the semantic and syntactic relations to their context. CSSEs, which are frequently made even by expert wiriters, significantly affect the reliability of spelling and grammar checkers. An existing Korean spelling and grammar checker developed by P University (KSGC 4.5) adopts hand-made correction rules for correcting CSSEs. The KSGC 4.5 is designed to obtain very high precision, which results in an extremely low recall. Our overall goal of previous works was to improve the recall without considerably lowering the precision, by generalizing CSSE correction rules that mainly depend on linguistic knowledge. A variety of rule-based methods has been proposed in previous works, and the best performance showed 95.19% of average precision and 37.56% of recall. This study thus proposes a statistics based method using a conditional probability model with dynamic window sizes. in order to further improve the recall. The proposed method obtained 97.23% of average precision and 50.50% of recall.

Context-Weighted Metrics for Example Matching (문맥가중치가 반영된 문장 유사 척도)

  • Kim, Dong-Joo;Kim, Han-Woo
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.43 no.6 s.312
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    • pp.43-51
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    • 2006
  • This paper proposes a metrics for example matching under the example-based machine translation for English-Korean machine translation. Our metrics served as similarity measure is based on edit-distance algorithm, and it is employed to retrieve the most similar example sentences to a given query. Basically it makes use of simple information such as lemma and part-of-speech information of typographically mismatched words. Edit-distance algorithm cannot fully reflect the context of matched word units. In other words, only if matched word units are ordered, it is considered that the contribution of full matching context to similarity is identical to that of partial matching context for the sequence of words in which mismatching word units are intervened. To overcome this drawback, we propose the context-weighting scheme that uses the contiguity information of matched word units to catch the full context. To change the edit-distance metrics representing dissimilarity to similarity metrics, to apply this context-weighted metrics to the example matching problem and also to rank by similarity, we normalize it. In addition, we generalize previous methods using some linguistic information to one representative system. In order to verify the correctness of the proposed context-weighted metrics, we carry out the experiment to compare it with generalized previous methods.

The Detection and Correction of Context Dependent Errors of The Predicate using Noun Classes of Selectional Restrictions (선택 제약 명사의 의미 범주 정보를 이용한 용언의 문맥 의존 오류 검사 및 교정)

  • So, Gil-Ja;Kwon, Hyuk-Chul
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.18 no.1
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    • pp.25-31
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    • 2014
  • Korean grammar checkers typically detect context-dependent errors by employing heuristic rules; these rules are formulated by language experts and consisted of lexical items. Such grammar checkers, unfortunately, show low recall which is detection ratio of errors in the document. In order to resolve this shortcoming, a new error-decision rule-generalization method that utilizes the existing KorLex thesaurus, the Korean version of Princeton WordNet, is proposed. The method extracts noun classes from KorLex and generalizes error-decision rules from them using the Tree Cut Model and information-theory-based MDL (minimum description length).

An Enhanced Text-Prompt Speaker Recognition Using DTW (DTW를 이용한 향상된 문맥 제시형 화자인식)

  • 신유식;서광석;김종교
    • The Journal of the Acoustical Society of Korea
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    • v.18 no.1
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    • pp.86-91
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    • 1999
  • This paper presents the text-prompt method to overcome the weakness of text-dependent and text-independent speaker recognition. Enhanced dynamic time warping for speaker recognition algorithm is applied. For the real-time processing, we use a simple algorithm for end-point detection without increasing computational complexity. The test shows that the weighted-cepstrum is most proper for speaker recognition among various speech parameters. As the experimental results of the proposed algorithm for three prompt words, the speaker identification error rate is 0.02%, and when the threshold is set properly, false rejection rate is 1.89%, false acceptance rate is 0.77% and verification total error rate is 0.97% for speaker verification.

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A Study on PLU (Phone-Likely Unit) for Korean Continuous Speech Recognition (강건한 한국어 연속음성인식을 위한 유사음소단일에 대한 연구)

  • Seo Jun-Bae;Kim Joo-Gon;Kim Min-Jung;Jung Ho-Youl;Chung Hyun-Yeol
    • Proceedings of the Acoustical Society of Korea Conference
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    • spring
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    • pp.37-40
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    • 2004
  • 본 논문은 한국어 연속음성인식에 효율적인 문맥의존 음향모델 수에 대한 연구로써 유사음소단위 수에 따른 인식 성능을 비교, 평가하였다. 기존에 본연구실에서는 48음소를 기본인식단위로 이용하고 있으나 연속음성인식의 경우 문맥종속모델이 사용되고 문맥종속모델은 변이 음을 고려한 음소가 이미 포함되어 있어 이를 고려하면 기본 음소를 줄이므로서 계산량의 감소와 인식 성능 향상을 기대할 수 있을 것으로 생각된다. 따라서 , 본 논문에서는 기존의 48음소와 이를 39음소로 줄여 인식실험에 사용하여 그 성능을 비교 평가하기로 하였다. 이를 위하여 다양한 태스크의 데이터베이스를 통합하여 부족한 문맥요소들을 확장한 후 인식실험을 수행하였다. 실험결과 변이음의 개수를 줄이면서도 인식 성능저하가 없음을 확인할 수 있었으며 연속 음성의 경우 39음소를 이용한 경우가 $10\%$정도의 향상된 인식성능을 얻을 수 있음을 확인할 수 있었다.

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Self-adaptation Service with Context-awareness on Active Network for Ubiquitous Computing Environment (유비쿼터스 컴퓨팅 환경을 위한 액티브네트워크상의 문맥인식성을 고려한 자치 적응성 서비스)

  • Hong Sungjune;Han Sunyoung
    • Journal of KIISE:Information Networking
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    • v.31 no.6
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    • pp.633-642
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    • 2004
  • A self-adaptation with context-awareness is needed within network to meet costumed services according a user's changing constraints. But the existing network has many difficulty in adding new functions because of slow standardization of network and slow deployment of new services. To solve this problem, an active network can support the suitable environment to add new function such as self- adaptation. Therefore, this Paper suggests Self Adaptation Service(SAS) using agent-based active network and the constraint-based Service Creation Environment(SCE) to support self-adaptation with context-awareness. SAS provides benefits to support the context-aware service and the fast deployment of new services.

A Study on Korean 4-connected Digit Recognition Using Demi-syllable Context-dependent Models (반음절 문맥종속 모델을 이용한 한국어 4 연숫자음 인식에 관한 연구)

  • 이기영;최성호;이호영;배명진
    • The Journal of the Acoustical Society of Korea
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    • v.22 no.3
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    • pp.175-181
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    • 2003
  • Because a word of Korean digits is a syllable and deeply coarticulatied in connected digits, some recognition models based on demisyllables have been proposed by researchers. However, they could not show an excellent recognition results yet. This paper proposes a recognition model based on extended and context-dependent demisyllables, such as a tri-demisyllable like a tri-phone, for the Korean 4-connected digits recognition. For experiments, we use a toolkit of HTK 3.0 for building this model of continuous HMMs using training Korean connected digits from SiTEC database and for recognizing unknown ones. The results show that the recognition rate is 92% and this model has an ability to improve the recognition performance of Korean connected digits.

A Study on Information Retrieval of Web Using Local Context Analysts Feedback (지역적 문맥 분석 피드백을 이용한 웹 정보검색에 관한 연구)

  • Kim, Young-Cheon;Lee, Sung-Joo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.6
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    • pp.745-751
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    • 2004
  • In conventional boolean retrieval systems, document ranking is not supported and similarity coefficients cannot be computed between queries and documents. The MMM(Max and Min Model), Paice and P-norm models have been proposed in the past to support the ranking facility for boolean retrieval systems. They have common properties of interpreting boolean operators softly In this paper we propose a new soft evaluation method for web Information retrieval using local context analysis feedback model. We also show through performance comparison that local contort analysis feedback is more efficient and effective than MMM, Paice and P-norm.