• Speech Sciences
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• v.8 no.4
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• pp.259-273
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• 2001

This study investigates the accentual realization of negation denying the presupposition of a previous utterance compared with that of a general negation. Specifically we study what types and positions of accent are realized in the speech of Korean speakers using English as a second language as well as English native speakers. Gussenhoven (1983) and Bolinger (1985, 1986) suggested that when presupposition of previous utterances is denied through negation, focal accent is assigned to empty (functional) words, rather than negative morphemes. The results of this study, however, show that negation sentences denying presupposition have accents not only on empty (functional) words but also on content words. Almost every English native speaker places an H* accent on the negative morphemes themselves (not, no, nothing, etc.) in general negation, as well as on the other lexical items-verbs and prepositions in our data-in negations denying presuppositions. On the other hand, Korean speakers hardly distinguish between the two kinds of negation sentences, unlike native speakers through accentual differences. Rather, they give accent an every content word, including the negative morphemes in both general negations and negations denying presuppositions. Therefore, the results of this study do not absolutely support the previous studies on the denial of presupposition.

• Language and Information
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• v.19 no.1
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• pp.37-57
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• 2015

This paper is primarily intended to provide a new insight on which the structural properties of As-Parenthetical constructions shown by Potts (2002) might be regarded as cross-linguistically common one. As a first attempt, it introduces the characteristics of Korean As-Parenthetical by carefully investigating them through the data, focusing on the similarities or differences between two languages with a constructional theoretical perspective. The paper here provides three properties of Korean as-clauses in the morphological and syntactic aspects. First, the morpheme 'as' in English as-clause would be realized as three different morphemes as a bound one. Korean as-clauses can be introduced by three different morphemes, '-tusi, -chelem, -taylo' and unlike that in English as-clauses, they behave as bound morphemes which do not stand alone. Even though they are attached into different morpho-syntactic stems, they do not make any meaning change only under this clause. Secondly, two syntactic types of as-clauses can also be found in Korean, similarly to those of English: CP-As type and Predicate-As type, depending on which types of gap they involve in. English has one more subtype of Predicate-As type (called inverted Predicate-As clause), while Korean does not show this subtype. Thirdly, the various mismatches attributed by the gap and the antecedent come from the constructional restrictions of as-clauses in Korean. In addition, the paper attempts to display various ambiguities from the as-clauses through disjoint references or negative sentences in As-Parenthetical constructions.

• Journal of Korean Academy of Nursing
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• v.44 no.3
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• pp.328-338
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• 2014

Purpose: This study was done to explore experiences of persons living through the periods of cancer diagnosis, treatment, and self-care. Methods: With permission, texts of 29 cancer survival narratives (8 men and 21 women, winners in contests sponsored by two institutes), were analyzed using Kang's Korean-Computerized-Text-Analysis-Program where the commonly used Korean-Morphological-Analyzer and the 21st-century-Sejong-Modern-Korean-Corpora representing laymen's Korean-language-use are connected. Experiences were explored based on words included in 100 highly-used-morphemes. For interpretation, we used 'categorizing words by meaning', 'comparing use-rate by periods and to the 21st-century-Sejong-Modern-Korean-Corpora', and highly-used-morphemes that appeared only in a specific period. Results: The most highly-used-word-morpheme was first-person-pronouns followed by, diagnosis treatment-related- words, mind-expression-words, cancer, persons-in-meaningful-interaction, living and eating, information-related-verbs, emotion-expression- words, with 240 to 0.8 times for layman use-rate. 'Diagnosis-process', 'cancer-thought', 'things-to-come-after-diagnosis', 'physician husband', 'result-related-information', 'meaningful-things before diagnosis-period', and 'locus-of-cause' dominated the life of the diagnosis-period. 'Treatment', 'unreliable-body', 'husband people mother physician', 'treatment-related-uncertainty', 'hard-time', and 'waiting-time represented experiences in the treatment-period. Themes of living in the self-care-period were complex and included 'living-as-a-human', 'self-managing-of-diseased-body', 'positive-emotion', and 'connecting past present future'. Conclusion: The results show that the experience of living for persons with cancer is influenced by each period's own situational-characteristics. Experiences of the diagnosis and treatment-period are negative disease-oriented while that of the self-care period is positive present-oriented.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.79-89
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• 2018

Sentiment analysis is a technique of text mining that extracts feelings of the person who wrote the sentence like movie review. The preliminary researches of sentiment analysis identify sentiments by using the dictionary which contains negative and positive words collected in advance. As researches on deep learning are actively carried out, sentiment analysis using deep learning model with morpheme or word unit has been done. However, this model has disadvantages in that the word dictionary varies according to the domain and the number of morphemes or words gets relatively larger than that of phonemes. Therefore, the size of the dictionary becomes large and the complexity of the model increases accordingly. We construct a sentiment analysis model using recurrent neural network by dividing input data into phoneme-level which is smaller than morpheme-level. To verify the performance, we use 30,000 movie reviews from the Korean biggest portal, Naver. Morpheme-level sentiment analysis model is also implemented and compared. As a result, the phoneme-level sentiment analysis model is superior to that of the morpheme-level, and in particular, the phoneme-level model using LSTM performs better than that of using GRU model. It is expected that Korean text processing based on a phoneme-level model can be applied to various text mining and language models.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.393-402
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• 2016

To compensate for limited the satisfaction survey currently conducted by Korea Metrological Administration (KMA), a sentiment analysis via a social networking service (SNS) can be utilized. From 2011 to 2014, with the sentiment analysis, Twitter who had commented 'KMA' had collected, then, using $Na{\ddot{i}}ve$ Bayes classification, we were classified into three sentiments: positive, negative, and neutral sentiments. An additional dictionary was made with morphemes appeared only in the positive, negative, and neutral sentiments of basic $Na{\ddot{i}}ve$ Bayes classification, thus the accuracy of sentiment analysis was improved. As a result, when sentiments were classified with a basic $Na{\ddot{i}}ve$ Bayes classification, the training data were reproduced about 75% accuracy rate. Whereas, when classifying with the additional dictionary, it showed 97% accuracy rate. When using the additional dictionary, sentiments of verification data was classified with about 75% accuracy rate. Lower classification accuracy rate would be improved by not only a qualified dictionary that has increased amount of training data, including diverse keywords related to weather, but continuous update of the dictionary. Meanwhile, contrary to the sentiment analysis based on dictionary definition of individual vocabulary, if sentiments are classified into meaning of sentence, increased rate of negative sentiment and change in satisfaction could be explained. Therefore, the sentiment analysis via SNS would be considered as useful tool for complementing surveys in the future.

• 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.