• 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 Society for Language and Information Conference
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• 2002.06a
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• pp.48-67
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• 2002

This paper is a report on part of the results of a research project entitled "Research and Model Development for a Multi-Lingual Lexical Database". It Is a six-year project in which we aim to construct a model of a multilingual lexical database of Korean, Chinese, Japanese, and English. Now we have finished the first two-year stage of the project In this paper, we present the goal of the project, the construction model of items in the lexical database, and the possible (semi-)automatic methods of acquisition of lexical information. As an appendix, we present some sample items of the database as an i1lustration.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.135-137
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• 2007

This study has experimented and materialized a computational lexical processing model which hybridizes full model and decomposition model as applying lexical acquisition, one of early stages of human lexical processes, to Korean. As the result of the study, we could simulate the lexical acquisition process of linguistic input through experiments and studying, and suggest a theoretical foundation for the order of acquitting certain grammatical categories. Also, the model of this study has shown proofs with which we can infer the type of the mental lexicon of the human cerebrum through fu1l-list dictionary and decomposition dictionary which were automatically produced in the study.

• MALSORI
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• no.50
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• pp.123-137
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• 2004

In this paper, we consider the implementation of connected digit recognition system using tree structured lexicon model. To implement efficiently the fixed or variable length digit recognition system, finite state network (FSN) is required. We merge the word network algorithm that implements the FSN with lexical tree search algorithm that is used for general speech recognition system for fast search and large vocabulary systems. To find the efficient modeling of digit recognition system, we investigate some performance changes when the lexical tree search is applied.

• The Journal of Korean Association of Computer Education
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• v.9 no.2
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• pp.89-99
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• 2006

This paper proposes a computational model which can simulate peculiar language phenomena observed in human lexical decision task. The model is designed to mimic major language phenomena such as frequency effect, lexical status effect, word similarity, and semantic priming effect. The experimental results show that the propose model replicated the major language phenomena and performed similar performance with that of human in LDT.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.881-891
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• 2023

Recently, various studies on malicious URL detection using artificial intelligence have been conducted, and most of the research have shown great detection performance. However, not only does classical machine learning require a process of analyzing features, but the detection performance of a trained model also depends on the data analyst's ability. In this paper, we propose a DL-ML Fusion Hybrid Model for malicious web site URL detection based on URL lexical features. the propose model combines the automatic feature extraction layer of deep learning and classical machine learning to improve the feature engineering issue. 60,000 malicious and normal URLs were collected for the experiment and the results showed 23.98%p performance improvement in maximum. In addition, it was possible to train a model in an efficient way with the automation of feature engineering.

• Language and Information
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• v.5 no.2
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• pp.21-37
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• 2001

Pustejovsky (1995) rekindled debate on the dual problems of how to represent lexical meaning and on the information that is to be encoded in a lexicon. For natural language processing such as machine translation, these are important issues. When a lexical-conceptual mismatch occurs in translation of corresponding words from two different languages, the appropriate representation of their meanings is very important. This paper proposes a new formalism for representing lexical entries by first analysing observable mismatches in comparable pairs of nouns, verbs, and adjectives in English and Korean. Inherent mis-interpretations and mis-readings in each pair are identified. Then, concept theories such as those presented by Ganter and Wille (1996) and Priss (1998) are extended in order to reflect the cognitivist view that meaning resides in concept, and also to incorporate the propositions of the so-called ‘multiple inheritance’system. An alternative to the formalism of Pustejovsky (1995) and Pollard & Sag (1994) is then proposed. Finally, representative examples of lexical mismatches are analysed using the new model.

• 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.

• Human Ecology Research
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• v.58 no.3
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• pp.429-445
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• 2020

This study developed and validated a 'Parent-child Lexical Interaction Scale for Preschoolers (PLIS-P)'. First, we developed the preliminary scale with 7 factors after reviewing previous literature related to vocabulary and literacy instruction for young children and reflected on feedback from child studies experts and mothers with young children. Subsequently, to validate the scale, the online survey was conducted on mothers with 5-to 6-year-old children who live in Seoul, Gyeonggi, Incheon, Gyeongsang, Chungcheong, Jeolla, Gangwon, and Jeju. Responses from 309 mothers were used to conduct exploratory and confirmatory factor analysis and correlation analysis. The results were as follows. First, the result of exploratory analysis showed that the model with 7 factors was satisfactory: (1) vocabulary exposure, (2) word elaboration, (3) scaffolding, (4) play activity, (5) conventional instruction, (6) word type awareness instruction, (7) word morphology instruction. Second, confirmatory factor analysis confirmed the good fit of the model. Third, the concurrent validity was confirmed by correlation analysis using EC-HOME. Last, the internal consistency reliability of each factor of PLIS-P was also confirmed. This study developed both a theoretical framework of parent-child lexical interaction and a Parent-child Lexical Interaction Scale for Preschoolers. This scale can be used by parents, practitioners, and researchers to acquire knowledge about interaction related to words between Korean parents and young children.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.7
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• pp.548-556
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• 1991

In this paper, Korean spoken continuous digits are ercognized using DHMM(Dynamic Hidden Markov Model) and lexical analysis to provide the base of developing voice dialing system. After segmentation by phoneme unit, it is recognized. This system can be divided into the segmentation section, the design of standard speech section, the recognition section, and the lexical analysis section. In the segmentation section, it is segmented using the ZCR, O order LPC cepstrum, and Ai, parameter of voice speech dectaction, which is changed according to time. In the standard speech design section, 19 phonemes or syllables are trained by DHMM and designed as a standard speech. In the recognition section, phomeme stream are recognized by the Viterbi algorithm.In the lexical decoder section, finally recognized continuous digits are outputed. This experiment shiwed the recognition rate of 85.1% using data spoken 7 times of 21 classes of 7 continuous digits which are combinated all of the occurence, spoken by 10 man.