• Journal of English Language & Literature
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• v.56 no.5
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• pp.851-870
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• 2010

The purpose of this paper is to investigate the distribution of /ju/ in British English, especially after the coronal sonorants /n, l, /r/. The sequence /ju/ is related with vowels such as /u/, /ʊ/, and /ʊ/, and has occasioned a variety of conflicting analyses or suggestions. One of those is in which context /j/ is deleted if we suppose that the underlying form is /ju/. The context differs according to the dialect we deal with. In British English, it is known that /j/ is deleted always after /r/, and usually after /l/ when it occurs in an unstressed word-medial syllable. To check this well-known fact I searched OED Online (the 2nd Edition, 1989) for those words which contain /n, l, r/ + /ju, jʊ, u, ʊ, (j)u, (j)ʊ/ in their pronunciations, using the search engine provided by OED Online. After removing some unnecessary words, I classified the collected words into several groups according to the preceding sonorant consonants, the positions, and the presence (or absence) of the stress, of the syllable where /ju/ occurs. The results are as follows: 1) the deletion of /j/ depends on the sonorant consonant which /ju/ follows, the position where it occurs, and the presence of the stress which /ju/ bears; 2) though the influence of the sonorant consonants is strong, the position and stress also have non-trivial effect on the deletion of /j/, that is, the word-initial syllable and the stressed syllable prefer the deletion of /j/, and word-medial and unstressed syllable usually retain /j/; 3) the stress and position factors play their own roles even in the context where the effect of /n, l, r/ is dominant.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.125-130
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• 2003

This paper presents a simple method of supervised word sense disambiguation using decision lists based on syntactic clues. This approach focuses on the syntactic relations between the given ambiguous word and surrounding words in context for resolving a given sense ambiguity. By identifying and utilizing only the single best disambiguation evidence in a given context instead of combining a set of clues, the algorithm decides the correct sense. Experiments with 10 Korean verbs show that adding syntactic clues to a basic set of surrounding context words improves 33% higher performance than baseline accuracy. In addition, our method using decision lists is 3% higher than a method using integration of all disambiguation evidences.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.75-86
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• 2006

Target word selection is one of the most important and difficult tasks in English-Korean Machine Translation. It effects on the translation accuracy of machine translation systems. In this paper, we present a new approach to select Korean target word for an English noun with translation ambiguities using multiple knowledge such as verb frame patterns, sense vectors based on collocations, statistical Korean local context information and co-occurring POS information. Verb frame patterns constructed with dictionary and corpus play an important role in resolving the sparseness problem of collocation data. Sense vectors are a set of collocation data when an English word having target selection ambiguities is to be translated to specific Korean target word. Statistical Korean local context Information is an N-gram information generated using Korean corpus. The co-occurring POS information is a statistically significant POS clue which appears with ambiguous word. The experiment showed promising results for diverse sentences from web documents.

• Journal of KIISE:Software and Applications
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• v.27 no.7
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• pp.769-783
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• 2000

In this paper, in order to disambiguate Korean noun word sense, we define a local context and explain how to extract it from a raw corpus. Following the intuition that two different nouns are likely to have similar meanings if they occur in the same local context, we use, as a clue, the word that occurs in the same local context where the target noun occurs. This method increases the usability of extracted knowledge and makes it possible to disambiguate the sense of infrequent words. And we can overcome the data sparseness problem by extending the verbs in a local context. The sense of a target noun is decided by the maximum similarity to the clues learned previously. The similarity between two words is computed by their concept distance in the sense hierarchy borrowed from WordNet. By reducing the multiplicity of clues gradually in the process of computing maximum similarity, we can speed up for next time calculation. When a target noun has more than two local contexts, we assign a weight according to the type of each local context to implement the differences according to the strength of semantic restriction of local contexts. As another knowledge source, we get a co-occurrence information from dictionary definitions and example sentences about the target noun. This is used to support local contexts and helps to select the most appropriate sense of the target noun. Through experiments using the proposed method, we discovered that the applicability of local contexts is very high and the co-occurrence information can supplement the local context for the precision. In spite of the high multiplicity of the target nouns used in our experiments, we can achieve higher performance (89.8%) than the supervised methods which use a sense-tagged corpus.

• ETRI Journal
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• v.32 no.5
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• pp.740-749
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• 2010

In a machine translation system, word sense disambiguation has an essential role in the proper translation of words when the target word can be translated differently depending on the context. Previous research on sense identification has mostly focused on adjacent words as context information. Therefore, in the case of nominal compounds, sense tagging of unit nouns mainly depended on other nouns surrounding the target word. In this paper, we present a practical method for the sense tagging of Korean unit nouns in a nominal compound. To overcome the weakness of traditional methods regarding the data sparseness problem, the proposed method adopts complement-predicate relation knowledge that was constructed for machine translation systems. Our method is based on a sentential form recovery technique, which recognizes grammatical relationships between unit nouns. This technique makes use of the characteristics of Korean predicative nouns. To show that our method is effective on text in general domains, the experiments were performed on a test set randomly extracted from article titles in various newspaper sections.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.246-259
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• 2023

This study explores the evolving landscape of consumer experiences in the context of pop-up stores, considering the shifts from product economy to service economy and now the experience economy. It investigates the factors influencing consumer word-of-mouth intentions by examining the interplay of pop-up store experiences, brand equity, brand charisma, and verbal intent. Using Schmitt's strategic experience modules and the Aaker brand equity model, the study employs quantitative methods and data analysis to uncover the relationships among these variables. Surprisingly, it finds limited associations between the aspects of the pop-up store experience and brand equity. However, it highlights the direct impact of brand equity on brand charisma, which subsequently influences consumers' intentions to share brand-related information. This research contributes to our understanding of word-of-mouth marketing for pop-up stores, filling a knowledge gap and offering valuable insights for academics and businesses navigating the evolving marketing landscape. It also emphasizes the significance of brand charisma in the context of transient in-store experiences and evolving consumer preferences.

• Journal of Korean Medical classics
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• v.32 no.1
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• pp.61-74
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• 2019

Objectives : The purpose of this study is to help select an appropriate word embedding method when analyzing East Asian traditional medicine texts as data. Methods : Based on prescription data that imply traditional methods in traditional East Asian medicine, we have examined 4 count-based word embedding and 2 prediction-based word embedding methods. In order to intuitively compare these word embedding methods, we proposed a "prescription generating game" and compared its results with those from the application of the 6 methods. Results : When the adjacent vectors are extracted, the count-based word embedding method derives the main herbs that are frequently used in conjunction with each other. On the other hand, in the prediction-based word embedding method, the synonyms of the herbs were derived. Conclusions : Counting based word embedding methods seems to be more effective than prediction-based word embedding methods in analyzing the use of domesticated herbs. Among count-based word embedding methods, the TF-vector method tends to exaggerate the frequency effect, and hence the TF-IDF vector or co-word vector may be a more reasonable choice. Also, the t-score vector may be recommended in search for unusual information that could not be found in frequency. On the other hand, prediction-based embedding seems to be effective when deriving the bases of similar meanings in context.

• Culinary science and hospitality research
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• v.20 no.5
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• pp.106-110
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• 2014

Little is known about the impact of ambience on customers' emotions, satisfaction, and word of mouth recommendation within the context of coffee shops. This study examined the relationships among ambience, emotions, customer satisfaction, and word of mouth recommendation in a coffee shop setting. A total of 303 visitors at 5 coffee shops in a Southwestern state in the U.S. completed questionnaires. Utilizing a structural equation modeling technique, this study demonstrated that ambience significantly influenced emotions and customer satisfaction. In addition, emotions significantly affected customer satisfaction and word of mouth recommendation.

• Journal of the Korean School Mathematics Society
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• v.9 no.2
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• pp.141-161
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• 2006

In this study, we classified word problems related to real life presented in elementary mathematics textbooks into five types of context problems(location, story, project, scrap, theme) suggested by Freudenthal(1991), and applied context problems to mathematics class to analyze the influence on students' mathematical belief and attitude. Also, we examined the types of context problems preferred according to academic performance and the reasons of preference within a group experiencing context problems. The results of the study are as follows. First, almost lessons in the mathematics textbook presents word problems related to real life, but the presenting method is inclined to a story type. Also, the problems with a story type are presented fragmentarily. Therefore, although these word problems are familiar to the students, they don't include contextual meanings and cannot induce enough mathematical motives and interests. Second, a lesson using context problems give a positive influence on their mathematics belief and attitude. It is also expected to give a positive influence on students' mathematics learning in the long run. Third, the preferred types of context problems and the reasons of preference are different according to the level of academic performance within the experimental group.

• ETRI Journal
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• v.28 no.6
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• pp.803-806
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• 2006

This letter addresses a structural disambiguation method for Korean adverbs based on the correlative relation constraints between adverbs and modifiees, and the morphological context information of sentences. Using the proposed method, we improved the dependency parsing accuracy of adverbs from 79.2 to 89%. The experimental result shows that the proposed method is especially expert in parsing adverbs which can modify multiple word classes or have a long distance dependency relation to their modifiees.