• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.259-267
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• 2021

Machine translators first appeared in the 1950s and made a leap in translation accuracy by applying the neural translation system in the 2010s. However, it is still having difficulty in translating complex sentences, which made it inconvenient to use machine translators as an English learning tool. Therefore, this study analyzed the usability of a machine translator as an English learning tool through a backtranslation experiment of as phrases. As analysis tools, Google Translator, Naver Papago, and Microsoft Translator, were used since they are representative machine translators using a neural translation system. As a result of the study, findings are: The usability was significantly different according to each as usage when using a machine translator. Accordingly, as usages in sentences were classified into high, ordinary, and low usability. Unlike previous studies, this study has a research contribution in analyzing the machine translator as a direct learning tool and quantifying the usability of the conjunction as.

• 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.

• Genomics & Informatics
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• v.19 no.3
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• pp.26.1-26.3
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• 2021

Previous approaches to create a controlled vocabulary for Japanese have resorted to existing bilingual dictionary and transformation rules to allow such mappings. However, given the possible new terms introduced due to coronavirus disease 2019 (COVID-19) and the emphasis on respiratory and infection-related terms, coverage might not be guaranteed. We propose creating a Japanese bilingual controlled vocabulary based on MeSH terms assigned to COVID-19 related publications in this work. For such, we resorted to manual curation of several bilingual dictionaries and a computational approach based on machine translation of sentences containing such terms and the ranking of possible translations for the individual terms by mutual information. Our results show that we achieved nearly 99% occurrence coverage in LitCovid, while our computational approach presented average accuracy of 63.33% for all terms, and 84.51% for drugs and chemicals.

• Journal of KIISE:Software and Applications
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• v.34 no.2
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• pp.95-103
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• 2007

This paper describes an English-to-Korean automatic translation system for patent documents which is constructed by a method customizing from a general domain to a specific domain. The customizing method consists of following steps: 1) linguistically studying about characteristics of patent documents, 2) extracting unknown words from large patent documents and terminologically constructing, 3) customizing the target language words of existing terms, 4) extracting and constructing patent translation patterns peculiar to patent documents, 5) customizing existing translation engine modules according to linguistic study about characteristics of patent documents, 6) evaluation of automatic translation results. The English-to-Korean patent machine translation system implemented by these customization steps shows a translation accuracy of 81.03% and is improving.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.345-352
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• 2006

The practical English-Korean machine translation system should be able to translate long sentences quickly and accurately. The intra-sentence segmentation method has been proposed and contributed to speeding up the syntactic analysis. This paper proposes the syntactic category prediction method using decision trees for getting accurate parsing results. In parsing with segmentation, the segment is separately parsed and combined to generate the sentence structure. The syntactic category prediction would facilitate to select more accurate analysis structures after the partial parsing. Thus, we could improve the parsing accuracy by the prediction. We construct features for predicting syntactic categories from the parsed corpus of Wall Street Journal and generate decision trees. In the experiments, we show the performance comparisons with the predictions by human-built rules, trigram probability and neural networks. Also, we present how much the category prediction would contribute to improving the translation quality.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.311-314
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• 1997

According to the development of industrial equipment such as semiconductor manufacturing machines, optical device, and precision machine tool, a high-precision translation system with wide range has been required. This paper describes a high-precision 2-axis translation system, which consists of microstage and global stage. In order to achieve the highresolution in the long range, some engineering techniques are used. Three linear guides with flexible coupling are adopted to reduce the motor vibration in the global stage. A simple elastic hinge structure activated by five PZT is applied to reduce the angular dev~atlon. As the result of combination of microstage and global stage associated with some engineering techniques, the 2-axis translation system can measure the 200 X 200 mrn range with the nanometer accuracy.

• The Transactions of the Korea Information Processing Society
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• v.5 no.3
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• pp.681-687
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• 1998

The paper presents a hybrid mcthod for disambiguation of the verb meaning in the machine translation. The presented verb translation algorithm is to perform the concept-based method and the statistics-based method simultaneously. It uses a collocation dictionary, WordNct and the statistical information extracted from corpus. In the transfer phase of the machine translation, it tries to find the target word of the source verb. If it fails, it refers to Word Net to try to find it by calculating word similarities between the logical constraints of the source sentence and those in the collocation dictionary. At the same time, it refers to the statistical information extracted from corpus to try to find it by calculating co-occurrence similarity knowledge. The experimental result shows that the algorithm performs more accurate verb translation than the other algorithms and improves accuracy of the verb translation by 24.8% compared to the collocation-based method.

• Korean Journal of Cognitive Science
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• v.15 no.2
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• pp.33-42
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• 2004

There are many syntactic similarities between Japanese and Korean language. These similarities enable us to build Japanese-Korean translation systems without depending cm sophisticated syntactic analysis and semantic analysis. To further improve translation accuracy, we have been developing a Japanese-Korean translation system using these similarities for several years. However, there still remain some problems with regard to translation of inflected words, processing of multi-translatable words and so on. In this paper, we propose a new method for Japanese-Koran machine translation by using the relationships of two neighboring words. To solve the problems, we investigate the connection rules of auxiliary verb priority. And we design the translation table, which consists of entry tables and connection form tables. for unambiguous words, we can translate a Japanese word to the corresponding Korean word in terms of direct-matching method by consulting the only entry table. Otherwise we have to evaluate the connection value computed from connection form tables and then we can select the most appropriate target word.

• Journal of KIISE:Software and Applications
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• v.27 no.3
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• pp.302-313
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• 2000

In English-Korean machine translation, transitive verb-objective collocation is utilized for accurate translation of an English verbal phrase into Korean. This paper presents an algorithm for correct verb translation based on the k-nearest neighbor learning. The semantic distance is defined on the WordNet for the k-nearest neighbor learning. And we also present algorithms for automatic collocation dictionary optimization. The algorithms extract transitive verb-objective pairs as training examples from large corpora and minimize the examples, considering the tradeoff between translation accuracy and example size. Experiments show that these algorithms optimized collocation dictionary keeping about 90% accuracy for a verb 'build'.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1307-1312
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• 2004

Word sense disambiguation is one of the most important problems in natural language processing research topics such as information retrieval and machine translation. Many approaches can be employed to resolve word ambiguity with a reasonable degree of accuracy. These strategies are: knowledge-based, corpus-based, and hybrid-based. This paper pays attention to the corpus-based strategy. The purpose of this paper is to compare three famous machine learning techniques, Snow, SVM and Naive Bayes in Word-Sense Disambiguation on Thai language. 10 ambiguous words are selected to test with word and POS features. The results show that SVM algorithm gives the best results in solving of Thai WSD and the accuracy rate is approximately 83-96%.