• Journal of KIISE
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• v.41 no.12
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• pp.1081-1089
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• 2014

Context-sensitive spelling-error correction methods are largely classified into rule-based methods and statistical data-based methods, the latter of which is often preferred in research. Statistical error correction methods consider context-sensitive spelling error problems as word-sense disambiguation problems. The method divides a vocabulary pair, for correction, which consists of a correction target vocabulary and a replacement candidate vocabulary, according to the context. The present paper proposes a method that integrates a word-phrase n-gram model into a conventional model in order to improve the performance of the probability model by using a correction vocabulary pair, which was a result of a previous study performed by this research team. The integrated model suggested in this paper includes a method used to interpolate the probability of a sentence calculated through each model and a method used to apply the models, when both methods are sequentially applied. Both aforementioned types of integrated models exhibit relatively high accuracy and reproducibility when compared to conventional models or to a model that uses only an n-gram.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.791-798
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• 2002

In this thesis. we present a new method for inducing a probabilistic dependency grammar (PDG) from text corpus. As words in Korean are composed of a set of more basic morphemes, there exist various dependency relations in a word. So, if the induction process does not take into account of these in-word dependency relations, the accuracy of the resulting grammar nay be poor. In comparison with previous PDG induction methods. the main difference of the proposed method lies in the fact that the method takes into account in-word dependency relations as well as inter-word dependency relations. To access the performance of the proposed method, we conducted an experiment using a manually-tagged corpus of 25,000 sentences which is complied by Korean Advanced Institute of Science and Technology (KAIST). The grammar induction produced 2,349 dependency rules. The parser with these dependency rules shoved 69.77% accuracy in terms of the number of correct dependency relations relative to the total number dependency relations for best-1 parse trees of sample sentences. The result shows that taking into account in-word dependency relations in the course of grammar induction results in a more accurate dependency grammar.

• Korean Journal of Cognitive Science
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• v.20 no.3
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• pp.291-307
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• 2009

This study investigated global and local characteristics of eye movement while 30 college students read easy and difficult Korean texts. It was found that readers who read the difficult text fixated longer for about 217ms and made shorter saccades of about 3.7 characters while readers who read the easy one fixated for about 190ms and made saccades of about 4.8 characters. Single fixation times and gaze durations in the difficult text were longer than those in the easy one(227ms vs. 195ms; 266ms vs. 210ms). In both easy and difficult texts, the effects of word frequency and eojeol length were found. In addition, the differences in fixation times according to word frequency and length were larger in the difficult text.

• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.946-950
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• 2010

Many sentiment categorization systems based on machine learning methods use morphological analyzers in order to extract linguistic features from sentences. However, the morphological analyzers do not generally perform well in a customer review domain because online customer reviews include many spacing errors and spelling errors. These low performances of the underlying systems lead to performance decreases of the sentiment categorization systems. To resolve this problem, we propose a feature extraction method based on simple longest matching of Eojeol (a Korean spacing unit) and phoneme patterns. The two kinds of patterns are automatically constructed from a large amount of POS (part-of-speech) tagged corpus. Eojeol patterns consist of Eojeols including content words such as nouns and verbs. Phoneme patterns consist of leading consonant and vowel pairs of predicate words such as verbs and adjectives because spelling errors seldom occur in leading consonants and vowels. To evaluate the proposed method, we implemented a sentiment categorization system using a SVM (Support Vector Machine) as a machine learner. In the experiment with Korean customer reviews, the sentiment categorization system using the proposed method outperformed that using a morphological analyzer as a feature extractor.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.3
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• pp.109-114
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• 2019

The similarity detecting method that is basically used in most plagiarism detecting systems is to use the frequency of shared words based on morphological analysis. However, this method has limitations on detecting accurate degree of similarity, especially when similar words concerning the same topics are used, sentences are partially separately excerpted, or postpositions and endings of words are similar. In order to overcome this problem, we have designed and implemented a plagiarism detecting system that provides more reliable similarity information by measuring sentence similarity and syntactic word similarity in addition to the conventional word similarity. We have carried out a comparison of on our system with a conventional system using only word similarity. The comparative experiment has shown that our system can detect plagiarized document that the conventional system can detect or cannot.

• Smart Media Journal
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• v.12 no.9
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• pp.19-27
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• 2023

Since morphemes are the smallest unit of meaning in Korean, it is necessary to develop an accurate morphemes analyzer to improve the performance of the Korean language model. However, most existing analyzers present morpheme analysis results by learning word unit tokens as input values. However, since Korean words are consist of postpositions and affixes that are attached to the root, even if they have the same root, the meaning tends to change due to the postpositions or affixes. Therefore, learning morphemes using word unit tokens can lead to misclassification of postposition or affixes. In this paper, we use morpheme-level tokens to grasp the inherent meaning in Korean sentences and propose a morpheme analyzer based on a sequence generation method using Transformer. In addition, a user dictionary is constructed based on corpus data to solve the out - of-vocabulary problem. During the experiment, the morpheme and morpheme tags printed by each morpheme analyzer were compared with the correct answer data, and the experiment proved that the morpheme analyzer presented in this paper performed better than the existing morpheme analyzer.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.5
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• pp.9-15
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• 2017

In the Digital Age, All Data used in the Internet is Digitized and Transmitted and Received Over a Communications Network. Therefore, it is Important to Transmit Data with Confidentiality and Integrity, Since Digital Data may be Tampered with and Tampered by Illegal users. Steganography is an Efficient Method for Ensuring Confidentiality and Integrity Together with Encryption Techniques. I Propose a Hangul Steganography Method that Inserts a Secret Message based on a Changing Insertion Position and a Changing Eojeol Size in a Cover Medium. Considering the Insertion Capacity of 3.35% and the File Size Change of 0.4% in Hangul Text Steganography, Experimental Results Show that the Jaro_score Value needs to be Maintained at 0.946.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.226-230
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• 2016

본 연구는 초등 저학년 읽기부진아동을 위한 한글 파닉스 교육의 기반을 확립하고자 1-2학년 교과서 고빈도 어절 531개를 기반으로 자소 및 음운규칙을 분석하였다. 연구결과, 자소-음소 일치 어절을 기반으로 하였을 때 초성에서 50번 이상 나타난 자소는 /ㄱ/, /ㄹ/, /ㄴ/, /ㅅ/, /ㅎ/, /ㅈ/이다. 중성에서 50번 이상 나타난 자소는 /ㅏ/, /ㅣ/, /ㅗ/, /ㅡ/, /ㅜ/이다. 종성에서 50번 이상 나타난 자소는 /ㄹ/, /ㄴ/, /ㅇ/이다. 자소와 음소가 불일치 된 어절을 기반으로 하였을 때 가장 많이 출현하는 음운규칙은 연음화 규칙이었다. 본 연구결과를 바탕으로 교과서를 기반으로 한 한글 파닉스 교육에 유용하게 사용될 수 있을 것이다.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.14-19
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• 2016

본 논문은 문장에서의 어절 간 의존관계가 성립될 때 의존소와 지배소가 어떠한 관계를 가지는지 의존 관계명을 부착하는 모델을 제안한다. 국내에서 한국어 의존구문분석에 관한 연구가 활발히 진행되고 있지만 의존 관계만을 결과로 제시하고 의존 관계명을 제공하지 않는 경우가 많았다. 따라서 본 논문에서는 의존경로(Dependency Path)와 음절의 의존 관계명 분포를 반영하는 음절 임베딩를 이용한 의존 관계명 부착모델을 제안한다. 문장에서 나올 수 있는 최적의 입력 열인 의존 경로(Dependency Path)를 순차 레이블링에서 좋은 성능을 나타내고 있는 bidirectional LSTM-CRFs의 입력 값으로 사용하여 의존 관계명을 결정한다. 제안된 기법은 자질에 대한 많은 노력 없이 의존 경로에 따라 어절 및 음절 단어표상(word embedding)만을 사용하여 순차적으로 의존 관계명을 부착한다. 의존 경로를 사용하지 않고 전체 문장의 어절 순서를 바탕으로 자질을 추출하여 CRFs로 분석한 기존 모델보다 의존 경로를 사용했을 때 4.1%p의 성능향상을 얻었으며, 의존 관계명 분포를 반영하는 음절 임베딩을 사용한 bidirectional LSTM-CRFs는 의존 관계명 부착에 최고의 성능인 96.01%(5.21%p 개선)를 내었다.

• Korean Journal of Cognitive Science
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• v.22 no.3
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• pp.327-345
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• 2011

In this paper, a new approach to Korean POS (Part-of-Speech) tagging is proposed. In previous works, a Korean POS tagger was regarded as a post-processor of a morphological analyzer, and as such a tagger was used to determine the most likely morpheme/POS sequence from morphological analysis. In the proposed approach, however, the POS tagger is supposed to generate the most likely morpheme and POS pair sequence directly from the given sentences. 398,632 eojeol POS-tagged corpus and 33,467 eojeol test data are used for training and evaluation, respectively. The proposed approach shows 96.31% of POS tagging accuracy.