• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.2
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• pp.65-70
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• 1998

In this paper, we will to check and to correct mis-recognized word using Eojeol information. First, we divided into 16 classes that constituents in a Eojeol after we analyzed Korean statement into Eojeol units. Eojeol-Constituent state diagram constructed these constitutents, find the Left-Right Connectivity Information. As analogized the speech of connectivity information, reduced the number of cadidate words and restricted case of morphological analysis for mis-recognition Eojeol. Then, we improved correction speed uisng heuristic information as the adjacency information for Eojeol each other. In the correction phase, construct Reverse-Order Word Dictionary. Using this, we can trace word dictionary regardless of mis-recongnition word position. Its results show that improvement of recognition rate from 97.03% to 98.02% and check rate, reduction of chadidata words and morpholgical analysis cases.

• KIISE Transactions on Computing Practices
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• v.22 no.3
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• pp.119-126
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• 2016

In this study, we verified the feasibility of a Korean morphological analyzer that uses a pre-analyzed Eojeol dictionary and syllable-based probabilistic model. For the verification, MACH and KLT2000, Korean morphological analyzers, were cloned with a pre-analyzed eojeol dictionary and syllable-based probabilistic model. The analysis results were compared between the cloned morphological analyzer, MACH, and KLT2000. The 10 million Eojeol Sejong corpus was segmented into 10 sets for cross-validation. The 10-fold cross-validated precision and recall for cloned MACH and KLT2000 were 97.16%, 98.31% and 96.80%, 99.03%, respectively. Analysis speed of a cloned MACH was 308,000 Eojeols per second, and the speed of a cloned KLT2000 was 436,000 Eojeols per second. The experimental results indicated that a Korean morphological analyzer that uses a pre-analyzed eojeol dictionary and syllable-based probabilistic model could be used in practical applications.

• Journal of Korean language education
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• v.29 no.3
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• pp.25-53
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• 2018

This study examined the influence of lexical factors on verbal Eojeol recognition. To meet the goal, forty-five L2 Korean learners and twenty-two Korean native speakers took Eojeol decision tasks measured with the lexical factors such as 'number of strokes', 'number of consonants and vowels', 'number of syllables', 'number of morphemes', 'whole Eojeol frequency', 'root frequency', 'first-syllable-sharing frequency', and 'number of dictionary meanings.' As a result, 'whole Eojeol frequency' was the most effective factor to predict Eojeol recognition reaction time for native speakers and L2 learners, which supports the full-list model. Other lexical factors influencing Eojeol recognition reaction time in L2 learners were different following their proficiency level.

• Journal of KIISE
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• v.42 no.1
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• pp.68-75
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• 2015

This paper proposes a method for the automatic word spacing of unsegmented Korean sentences. In our method, eojeol monograms are used for word spacing as opposed to the syllable n-grams that have been used in previous studies. The use of a Korean morphological analyzer is limited to the correction of typical word spacing errors. Our method gives a 98.06% syllable accuracy and a 94.15% eojeol recall, when 10-fold cross-validated with the Sejong corpus, after filtering out non-hangul eojeols. The processing rate is 250K eojeols or 1.8 MB per second on a typical personal computer. Syllable accuracy and eojeol recall are related to the size of the eojeol dictionary, better performance is expected with a bigger corpus.

• Phonetics and Speech Sciences
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• v.10 no.4
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• pp.111-119
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• 2018

This paper proposes a method to improve speech recognition performance by extracting various pronunciations of the pseudo-morpheme unit from an eojeol unit corpus and generating a new recognition unit considering pronunciation variations. In the proposed method, we first align the pronunciation of the eojeol units and the pseudo-morpheme units, and then expand the pronunciation dictionary by extracting the new pronunciations of the pseudo-morpheme units at the pronunciation of the eojeol units. Then, we propose a new recognition unit that relies on pronunciation by tagging the obtained phoneme symbols according to the pseudo-morpheme units. The proposed units and their extended pronunciations are incorporated into the lexicon and language model of the speech recognizer. Experiments for performance evaluation are performed using the Korean speech recognizer with a trigram language model obtained by a 100 million pseudo-morpheme corpus and an acoustic model trained by a multi-genre broadcast speech data of 445 hours. The proposed method is shown to reduce the word error rate relatively by 13.8% in the news-genre evaluation data and by 4.5% in the total evaluation data.

• Journal of KIISE
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• v.43 no.3
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• pp.362-369
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• 2016

This study is directed toward the design of a hybrid algorithm for syllable-based Korean POS tagging. Previous syllable-based works on Korean POS tagging have relied on a sequence labeling method and mostly used only a machine learning method. We present a new algorithm integrating a machine learning method and a pre-analyzed dictionary. We used a Sejong tagged corpus for training and evaluation. While the machine learning engine achieved eojeol precision of 0.964, the proposed hybrid engine achieved eojeol precision of 0.990. In a Quiz domain test, the machine learning engine and the proposed hybrid engine obtained 0.961 and 0.972, respectively. This result indicates our method to be effective for Korean POS tagging.

• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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• pp.173-183
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• 2003

Morphological analysis is the most widely used method for extracting nouns from Korean texts. For every Eojeol, in order to extract nouns from it, a morphological analyzer performs frequent dictionary lookup and applies many morphonological rules, therefore it requires many operations. Moreover, a morphological analyzer generates all the possible morphological interpretations (sequences of morphemes) of a given Eojeol, which may by unnecessary from the noun extraction`s point of view. To reduce unnecessary computation of morphological analysis from the noun extraction`s point of view, this paper proposes a method for Korean noun extraction considering noun occurrence characteristics. Noun patterns denote conditions on which nouns are included in an Eojeol or not, which are positive cues or negative cues, respectively. When using the exclusive information as the negative cues, it is possible to reduce the search space of morphological analysis by ignoring Eojeols not including nouns. Post-noun syllable sequences(PNSS) as the positive cues can simply extract nouns by checking the part of the Eojeol preceding the PNSS and can guess unknown nouns. In addition, morphonological information is used instead of many morphonological rules in order to recover the lexical form from its altered surface form. Experimental results show that the proposed method can speed up without losing accuracy compared with other systems based on morphological analysis.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.279-284
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• 2008

본 연구에서는 어절패턴을 이용하는 새로운 방식의 한국어 형태소 분석기 KGuru-MA에 대해서 설명한다. KGuru-MA는 품사 부착 말뭉치에서 개방어를 생략하여 어절 패턴을 반자동으로 학습하여 어절 패턴 사전과 형태소 확률 정보 사전을 구성한 후, 이 사전을 이용하여 형태소를 분석한다. 본 형태소 분석기는 어절패턴을 사용하여 형태소 분석하기 때문에 기존 형태소 분석기에 존재하는 접속검사 과정이 생략된다. 또한, 형태소 분석 과정이 기존의 형태소 분석기에 비해 단순하여 기초 자연언어 처리 시스템이 가지는 강건성을 보장한다. 본 연구는 "21세기 세종기획 3차년도 말뭉치"를 이용한 실험 결과, 기존 형태소 분석기 못지 않은 성능을 보였다.

• Annual Conference on Human and Language Technology
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• 2004.10d
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• pp.112-117
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• 2004

형태소 분석에서 사용되는 사전은 형태소와 품사 정보를 수록하고 있다. 단어가 한 개의 형태소로 구성되는 굴절어는 대부분의 단어가 어휘형태소의 기본형과 일치되기 때문에 형태소 분석 알고리즘은 사전 탐색과 형태론적 변형을 통해 입력 단어와 어휘형태소를 일치시키는 과정으로 기술된다. 이에 비해, 교착어는 입력 어절이 형태소 사전의 어휘형태소와 일치하지 않기 때문에 어절 자체가 형태소 사전에 포함되지 않아서 굴절어에 비해 상대적으로 형태소 분석 알고리즘의 복잡도가 높고 분석 시간이 오래 걸리는 단점이 있다. 본 논문에서는 고빈도 어절에 대한 기분석 어절 사전을 구축하여 형태소 분석 속도를 개선하고, 사용자가 어절 사전에 새로운 어절을 추가하거나 어절 사전에 수록된 분석 결과를 수정할 수 있는 어절 사전에 의한 형태소 분석 방법을 제안한다. 구체적인 방법론으로써 형태소 분석 결과를 저장하는 기분석 어절 사전의 크기를 최소화하기 위해 분석 결과를 생성하는데 필요한 최소한의 정보만을 인코딩하는 방법을 사용한다.

• The KIPS Transactions:PartB
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• v.8B no.1
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• pp.98-104
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• 2001

본 논문에서는 어절 생성 사전을 이용한 한국어 철자 교정을 제안한다. 어절 생성 사전은 두 문자열 간 음절 특성이 고려된 편집 거리 계산을 기반으로 탐색되어 언어와 오류 유형에 의존적인 정보를 이용하지 않고 오류 어절에 대한 후보 어절을 생성한다. 또한 교정된 어절들의 가능한 형태소 분석들을 산출하여 후보들 간의 순위 계산 시에 재차 형태소 분석을 수행하지 않고 언어 정보를 적용할 수 있다. 본 논문에서 제안하는 철자 교정은 두 단계로 구성된다. 첫째, 오류 어절로부터 가능한 오류 정정 어간들을 계산한다. 둘째, 계산된 어간들로부터 어절 생성 사전을 탐색하여 원형 후보 어절들을 생성한다. 또한 품사 태깅과 공기 정보를 사용하여 오류 수정된 결과의 순위를 매긴다. 본 시스템의 자동 철자 교정 성능을 평가한 결과 3,000개의 어절에서 시험한 결과 단어 수준으로 93%가 옳게 교정되었다.