• 한국인지과학회:학술대회논문집
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• 한국인지과학회 2002년도 춘계학술대회
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• pp.111-116
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• 2002

The regularity effect for printed word recognition and naming depends on ambiguities between single letters (small grain-size) and their phonemic values. As a given word is repeated and becomes more familiar, letter-aggregate size (grain-size) is predicted to increase, thereby decreasing the ambiguity between spelling pattern and phonological representation and, therefore, decreasing the regularity effect. Lexical decision and naming tasks studied the effect of repetition on the regularity effect for words. The familiarity of a word from was manipulated by presenting low and high frequency words as well as by presenting half the stimuli in mixed upper- and lowercase letters (an unfamiliar form) and half in uniform case. In lexical decision, the regularity effect was initially strong for low frequency words but became null after two presentations; in naming it was also initially strong but was merely reduced (although still substantial) after three repetitions. Mixed case words were recognized and named more slowly and tended to show stronger regularity effects. The results were consistent with the primary hypothesis that familiar word forms are read faster because they are processed at a larger grain-size, which requires fewer operations to achieve lexical selection. Results are discussed in terms of a neurobiological model of word recognition based on brain imaging studies.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 1995년도 제7회 한글 및 한국어 정보처리 학술대회
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• pp.211-214
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• 1995

At what level of orthographic representation is phonology linked in thelexicon? Is it at the whole word level, the syllable level, letter level, etc? This question can be addressed by comparing the two scripts used in Korean, logographic Hanmoon and alphabetic/syllabic Hangul, on a task where judgements must be made about the phonology of a visually presented word. Four experiments are reported using a "homophone decision task" and manipulating the sub-lexical relationship between orthography and phonology in Hanmoon and Hangul, and the lexical status of the stimuli. Hangul words showed a much higher error rate in judging whether there was another word identically pronounced than both Hangul nonwords and Hanmoon words. It is concluded that the relationship between orthography and phonology in the lexicon differs according tn the type of script owing to the availability of sub-lexical information: the process of making a homophone derision is based on a spread of activation exclusively among lexical entries, from orthography to phonology and vice versa (called "Orthography-Phonology-Orthography Rebound" or "OPO Rebound"). The results are explained within the mulitilevel interactive activation model with orthographic units linked to phonological units at each level.

• 전자공학회논문지B
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• 제30B권12호
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• pp.21-30
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• 1993

In this paper, we propose a connected Korean digit recohnition system employing neural networks and lexical constraints of the Korean digits. In the proposed recognition system, firstly, each frame of digit string is labelled by phoneme classification neural networks.which are trained with the reference phoneme segments extracted form an isolated digit based on the position information. And, the frame labels are combined with each other for constructing the phoneme segments. Then, these segments are combined to form a digit candidate using the digit combination rules. The digit candidate is decided based on the condition for digit decision. If the condition is not satisfied, the digit candidate is further recognized using the digit decision neural network in the next step. In our approach, the neural networks are trained with 10 isolated digits uttered by 5 male speakers. To investigate the performance of the proposed recognition system, an intensive computer simulation on the 30 connected digit strings uttered by 5 male speakers is performed. The simulation result indicates that 95.6% digit recognition rate and 82% digit string recognition rate are provided by the proposed Korean digit recognition system.

• 한국인지과학회:학술대회논문집
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• 한국인지과학회 2006년도 춘계학술대회
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• pp.33-37
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• 2006

본 논문은 어휘판단과제(LDT: Lexical Decision Task)시 나타나는 여러 언어현상 중 단어빈도효과(word frequency effect)와 단어유사성효과(word similarity effect)를 한국어에 적용시켜 인간과 계산주의적 모델을 통해 실험하고, 결과를 비교하였다. 실험결과 인간과 계산주의적 모델 각각 한국어에 대해 단어빈도효과와 단어 유사성효과를 보였으며, 인간의 실험결과와 계산주의적 모델의 결과가 유의미한 유사성을 나타내었다.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2007년도 한국음성과학회 공동학술대회 발표논문집
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• pp.173-175
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• 2007

The lexical decision task (LDT) commonly postulates the activation of semantic level. However, there are few studies for the feedback effect from semantic level. The purpose of the present study is to investigate whether the feedback effect from semantic level is facilitatory or inhibitory in Korean LDT. In Experiment 1, we manipulated the number of phonological syllable neighbors (PSN) and the number of semantic neighbors (SEN) orthogonally while orthographic syllable neighbor (OSN) is dense. In the results, the significant facilitatory effect was shown in words with many SEN. In Experiment 2, we examined same conditions as Experiment 1 but OSN was sparse. Although the similar lexical decision latency pattern was shown, there was no statistical significance. These results can be explained by the feedback activation from semantic level. If a target has many SENs and many PSNs, it receives more feedback activation from semantic level than a target with few SENs and PSNs.

• Journal of Information Processing Systems
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• 제12권3호
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• pp.358-380
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• 2016

In order to considerably reduce the ambiguity rate, we propose in this article a disambiguation approach that is based on the selection of the right diacritics at different analysis levels. This hybrid approach combines a linguistic approach with a multi-criteria decision one and could be considered as an alternative choice to solve the morpho-lexical ambiguity problem regardless of the diacritics rate of the processed text. As to its evaluation, we tried the disambiguation on the online Alkhalil morphological analyzer (the proposed approach can be used on any morphological analyzer of the Arabic language) and obtained encouraging results with an F-measure of more than 80%.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2006년도 춘계학술발표논문집
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• pp.391-393
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• 2006

본 논문은 어휘판단과제(LDT: Lexical Decision Task)시 나타나는 여러 언어현상 중 단어빈도효과(word frequency effect)와 단어유사성효과(word similarity effect)를 한국어에 적용시켜 인간과 계산 주의적 모델을 통해 실험하고, 결과를 비교하였다. 실험결과 인간과 계산주의적 모델 각각 한국어에 대해 단어빈도효과와 단어 유사성효과를 보였으며, 인간의 실험결과와 계산주의적 모델의 결과가 유의미한 유사성을 나타내었다.

• 문화기술의 융합
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• 제7권3호
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• pp.433-438
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• 2021

본 연구는 자동화된 어휘 분석 프로그램인 TAALES를 활용하여 영어능력 수준과 어휘의 정교화 지표간의 관계를 살펴본다. 본 연구에서는 ICNALE 코퍼스에 포함된 한국인 대학생이 작성한 에세이 600개를 분석하였다. 본 연구에서는 영어 수준별로 어휘의 정교화 특성이 통계적으로 유의미하게 다르게 나타나는가를 살펴보기 위해 다변량 분산분석이 수행되었다. 다변량 분산분석 결과 어휘의 정교화 지표가 통계적으로 유의미한 것으로 나타났으며, 이는 상. 중. 하의 세 개의 집단에 따라 어휘의 정교화 지표의 평균벡터에 유의미한 차이가 있고 상. 중. 하의 세 집단은 어휘의 정교화 지표의 사용이 유의미하게 다르다는 것을 보여준다. 세 집단 간에 가장 큰 차이를 보인 어휘의 정교화 지표는 내용어의 빈도, 내용어의 친숙도, 기능어의 어휘 결정 반응 평균 시간, 상위어 동사, 기능어의 워드 네이밍 반응시간, 내용어의 어휘 습득 연령으로 나타났다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제29권9호
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• pp.654-668
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• 2002

In this paper, we present an empirical study for improving the Korean text chunking based on machine learning and feature set selection approaches. We focus on two issues: the problem of selecting feature set for Korean chunking, and the problem of alleviating the data sparseness. To select a proper feature set, we use a heuristic method of searching through the space of feature sets using the estimated performance from a machine learning algorithm as a measure of "incremental usefulness" of a particular feature set. Besides, for smoothing the data sparseness, we suggest a method of using a general part-of-speech tag set and selective lexical information under the consideration of Korean language characteristics. Experimental results showed that chunk tags and lexical information within a given context window are important features and spacing unit information is less important than others, which are independent on the machine teaming techniques. Furthermore, using the selective lexical information gives not only a smoothing effect but also the reduction of the feature space than using all of lexical information. Korean text chunking based on the memory-based learning and the decision tree learning with the selected feature space showed the performance of precision/recall of 90.99%/92.52%, and 93.39%/93.41% respectively.

• 말소리와 음성과학
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• 제1권4호
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• pp.17-27
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• 2009

Despite many studies on the production and perception of L2 phonemes, studies on how such phonemes are encoded lexically remain scarce. The aim of this study is to examine whether L2 learners have a perceptual problem with L2 suprasegmentals which are not present in their L1, or if they are able to perceive but not able to encode them in their lexicon. Specifically, Korean learners were tested to see if they could discriminate the vowel length differences in Japanese at the psychoacoustic level through a simple AX discrimination task. Then, a speeded lexical decision task with high phonetic variability was conducted to see whether they could use such contrasts lexically. The results showed that Korean learners of Japanese have no difficulties in discriminating Japanese vowel length contrast, but they are unable to encode such contrast in their phonological representation, even with long L2 exposure.