• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.124-129
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• 2006

This parer presents a method of ranking translation word selection for Korean verbs based on lexical knowledge contained in a bilingual Korean-English dictionary and WordNet that are easily obtainable knowledge resources. We focus on deciding which translation of the target word is the most appropriate using the measure of semantic relatedness through the 45 extended relations between possible translations of target word and some indicative clue words that play a role of predicate-arguments in source language text. In order to reduce the weight of application of possibly unwanted senses, we rank the possible word senses for each translation word by measuring semantic similarity between the translation word and its near synonyms. We report an average accuracy of $51\%$ with ten Korean ambiguous verbs. The evaluation suggests that our approach outperforms the default baseline performance and previous works.

• The Transactions of the Korea Information Processing Society
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• v.5 no.9
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• pp.2361-2368
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• 1998

This paper analyzes Korean spoken language and describes the machine translation o[ Korean to-English spoken language based on semantic patterns, In Korean-to-English machine translation. ambiguity of Korean sentence analysis using syntactic information can be resolved by semantic patterns, Therefore, for machine translation of spoken language, we estabilish the system based on semantic patterns extracted from Korean scheduling domain, This system obtains the robustness by skip ability of syllables in analysis of Korean sentence and we add options to semantic patterns in order to reduce pattern numbers, The data used [or the experiment are scheduling domain and performance of Korean-to-English translation is 88%.

• Korean Journal of English Language and Linguistics
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• v.3 no.4
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• pp.589-609
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• 2003

The purpose of this paper is to show that lexical equivalency in translation can be achieved when it is based on semantic prosodies of lexical items. This paper examines the semantic prosodies of two seemingly synonymous English adjectives ‘empty’ and ‘blank’ on the basis of the corpus given in Cobuild English Collocations on CD-ROM and proposes that they are different in terms of spatial dimensions. Thus when a Korean equivalent pin derived from the verb pita is translated into English, syntagmatic phraseological environments of the Korean adjective must be taken into account to attain the equivalency of the source and target languages. Relevant Korean corpus was taken from the 21st Century Sejong Plan (2002). Out of 12 examples of pin konggan, five appear to be equivalent to ‘blank’ and seven to ‘empty.’ The five to seven ratio in different usage indicates that the equivalency problem concerning the lexical item pin is not a trivial matter in translation.

• Journal of Information Technology Applications and Management
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• v.9 no.2
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• pp.71-80
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• 2002

Automatic language translation has greatly advanced due to the increased user needs and Information retrieval in WWW. This paper utilizes the integrated knowledge bases of noun and verb networks for automatic translation of ambiguous words in the Korean sentences, through the selectional restriction relation in the sentences. And this paper presents the method to verify validity of Korean noun semantic networks that are used for the construction of the selectional restriction relation by applying the networks to the syntactic and semantic properties Integration of Korean Noun Networks into the SENKOV system will provide the accurate and efficient knowledge bases for the semantic analysis of Korean NLP.

• Asia Marketing Journal
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• v.12 no.4
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• pp.1-25
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• 2011

Brand names are often considered as a part of product and important extrinsic cues of product evaluation, when consumers make purchasing decisions. For a company, brand names are also important assets. Building a strong brand name in the Chinese commonwealth is a main challenge for many global companies. One of the first problem global company has to face is how to translate English brand name into Chinese brand name. It is very difficult decision because of cultural and linguistic differences. Western languages are based on an alphabet phonetic system, whereas Chinese are based on ideogram. Chinese speakers are more likely to recall stimuli presented as brand names in visual rather than spoken recall, whereas English speakers are more likely to recall the names in spoken rather than in visual recall. We interpret these findings in terms of the fact that mental representations of verbal information in Chinese are coded primarily in a visual manner, whereas verbal information in English is coded by primarily in a phonological manner. A key linguistic differences that would affect the decision to standardize or localize when transferring English brand name to Chinese brand name is the writing system. Prior Chinese brand naming research suggests that popular Chinese naming translations foreign companies adopt are phonetic, semantic, and phonosemantic translation. The phonetic translation refers to the speech sound that is produced, such as the pronunciation of the brand name. The semantic translation involves the actual meaning of and association made with the brand name. The phonosemantic translation preserves the sound of the brand name and brand meaning. Prior brand naming research has dealt with word-level analysis in examining English brand name that are desirable for improving memorability. We predict Chinese brand name suggestiveness with different translation methods lead to different levels of consumers' evaluations. This research investigates the structural linguistic characteristics of the Chinese language and its impact on the brand name evaluation. Otherwise purpose of this study is to examine the effect of brand concept on the evaluation of brand name. We also want to examine whether the evaluation is moderated by Chinese translation types. 178 Taiwanese participants were recruited for the research. The following findings are from the empirical analysis on the hypotheses established in this study. In the functional brand concept, participants in Chinese translation by semantic were likely to evaluate positively than Chinese translation by phonetic. On the contrary, in the symbolic brand concept condition, participants in Chinese translation by phonetic evaluated positively than by semantic. And then, we found Chinese translation by phonosemantic was most favorable evaluations regardless of brand concept. The implications of these findings are discussed for Chinese commonwealth marketers with respect to brand name strategies. The proposed model helps companies to effectively select brand name, making it highly applicable for academia and practitioner. name and brand meaning. Prior brand naming research has dealt with word-level analysis in examining English brand name that are desirable for improving memorability. We predict Chinese brand name suggestiveness with different translation methods lead to different levels of consumers' evaluations. This research investigates the structural linguistic characteristics of the Chinese language and its impact on the brand name evaluation. Otherwise purpose of this study is to examine the effect of brand concept on the evaluation of brand name. We also want to examine whether the evaluation is moderated by Chinese translation types. 178 Taiwanese participants were recruited for the research. The following findings are from the empirical analysis on the hypotheses established in this study. In the functional brand concept, participants in Chinese translation by semantic were likely to evaluate positively than Chinese translation by phonetic. On the contrary, in the symbolic brand concept condition, participants in Chinese translation by phonetic evaluated positively than by semantic. And then, we found Chinese translation by phonosemantic was most favorable evaluations regardless of brand concept. The implications of these findings are discussed for Chinese commonwealth marketers with respect to brand name strategies. The proposed model helps companies to effectively select brand name, making it highly applicable for academia and practitioner.

• Journal of KIISE
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• v.42 no.1
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• pp.54-67
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• 2015

Nowadays, studies on the fusion of Semantic Web technologies are being carried out to promote the interoperability and value of sensor data in an IoT environment. To accomplish this, the semantic translation of sensor data is essential for convergence with service domain knowledge. The existing semantic translation technique, however, involves translating from static metadata into semantic data(RDF), and cannot properly process real-time and large-scale features in an IoT environment. Therefore, in this paper, we propose a technique for translating large-scale streaming sensor data generated in an IoT environment into semantic data, using real-time and parallel processing. In this technique, we define rules for semantic translation and store them in the semantic repository. The sensor data is translated in real-time with parallel processing using these pre-defined rules and an ontology-based semantic model. To improve the performance, we use the Apache Storm, a real-time big data analysis framework for parallel processing. The proposed technique was subjected to performance testing with the AWS observation data of the Meteorological Administration, which are large-scale streaming sensor data for demonstration purposes.

• Korean Journal of Computational Design and Engineering
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• v.9 no.3
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• pp.192-202
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• 2004

This paper introduces an approach to semantic mapping using Product ontology for CPC environment. In CPC environment, it is necessary that the participants in a product life cycle should share the same understanding about the semantic of product terms. For example, they should know that although 'COMPONENT' and 'ITEM' are different word-expressions, they could have the same meaning. In order to handle such terms in the information system, it is desirable that the system automatically recognizes that the terms have the same semantics. Serving this purpose, we described an ontology design methodology using first order logic, knowledge interchange format, and knowledge engineering process. In our approach, we investigated domain knowledge of the Bill Of Material, and then designed Product ontology of it. Based on the ontology, we described syntactic translation, semantic translation, and semantic mapping procedure with an example.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.111-118
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• 2022

This research article presents the work that is related to the application of different machine learning based similarity techniques on religious text for identifying similarities and differences among its various translations. The dataset includes 10 different English translations of verses (Arabic: Ayah) of two Surahs (chapters) namely, Al-Humazah and An-Nasr. The quantitative similarity values for different translations for the same verse were calculated by using the cosine similarity and semantic similarity. The corpus went through two series of experiments: before pre-processing and after pre-processing. In order to determine the performance of machine learning based similarities, human annotated similarities between translations of two Surahs (chapters) namely Al-Humazah and An-Nasr were recorded to construct the ground truth. The average difference between the human annotated similarity and the cosine similarity for Surah (chapter) Al-Humazah was found to be 1.38 per verse (ayah) per pair of translation. After pre-processing, the average difference increased to 2.24. Moreover, the average difference between human annotated similarity and semantic similarity for Surah (chapter) Al-Humazah was found to be 0.09 per verse (Ayah) per pair of translation. After pre-processing, it increased to 0.78. For the Surah (chapter) An-Nasr, before preprocessing, the average difference between human annotated similarity and cosine similarity was found to be 1.93 per verse (Ayah), per pair of translation. And. After pre-processing, the average difference further increased to 2.47. The average difference between the human annotated similarity and the semantic similarity for Surah An-Nasr before preprocessing was found to be 0.93 and after pre-processing, it was reduced to 0.87 per verse (ayah) per pair of translation. The results showed that as expected, the semantic similarity was proven to be better measurement indicator for calculation of the word meaning.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.265-270
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• 2022

San Mao is a prominent Taiwanese female writer. She created many works in a unique style based on what she experienced and studied through her experience of studying abroad. Among them, 『Daocaoren Shouj i』 is a prose that records episodes of overseas life. There are two Korean translations of this work. Different translators used different translation strategies. TT1 used a lot of semantic translation to emphasize and translate a lot of exotic feelings. On the other hand, TT2 pursued natural translation by using a communicative translation strategy. In addition, the peculiarity is that both translations attempted 'creative translation' and intentionally 'transformed' and translated. This creative translation helps the reader to immerse and understand the work. With this study as an opportunity, we hope that various follow-up studies on San Mao's translation will be conducted.

• The Transactions of the Korea Information Processing Society
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• v.3 no.7
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• pp.1792-1802
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• 1996

The analysis of prepositional phrases in English-to Korean machine translation has problem on the PP-attachment resolution, semantic analysis, and acquisition of information. This paper presents an analysis system for prepositional phrases, which solves the problem. The analysis system consists of the PP-attachment resolution hybrid system, semantic analysis system, and semantic feature generator that automatically generates input information. It provides objectiveness in analyzing prepositional phrases with the automatic generation of semantic features. The semantic analysis system enables to generate natural Korean expressions through selection semantic roles of prepositional phrases. The PP-attachment resolution hybrid system has the merit of the rule-based and neural network-based method.