Semantic change analysis of Korean verbs based on massive culture corpus data

Lou, Jianying; Zhang, Yanqing

doi:10.1007/s00779-019-01328-8

Cited by 3 publications

(4 citation statements)

References 25 publications

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“…It has been 13 years since the construction of the Aozora Library began in 1997, and it has reached a considerable size, containing 10,752 literary works. Most of the works of some of these writers have been entered into the database [18]. The wide range of subjects in the Aozora Library, including novels, essays, travelogues, book reviews, memoirs, and various other subjects, ensures the diversity of the corpus and allows for the study of specific linguistic phenomena from a variety of different linguistic materials.…”

Section: Related Workmentioning

confidence: 99%

Development and Teaching Application of Japanese Corpus Based on Big Data

Liu

Hua

2022

Wireless Communications and Mobile Computing

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The establishment of English and French corpora has provided great convenience for language application and research. However, in the field of Japanese language research, the construction of Japanese corpus has been slow due to the limitation of Japanese markup format. This paper discusses the scientificity, rationality, feasibility, and construction of “microcorpus” and “specialized corpus” for the application of Japanese corpus in Japanese language teaching. Textual corpus is a corpus that contains all text but no media such as audio and pictures, and it is mostly used in teaching. The Japanese language corpus is a corpus created by analyzing the existing Japanese classroom teaching situation in response to the current reform of Japanese language teaching mode. It is important for teaching Japanese in colleges and universities because it can help students to learn independently and improve their learning initiative and motivation. The teaching-based text corpus is a small corpus for Japanese language teaching and learning, which is derived from the teaching process. The corpus can be created independently on an ordinary PC machine through software, and the collected corpus can be imported into the corpus and used, which provides another way to improve students’ interest and learning ability on their own. Based on the search software, this paper selects representative novels from the Aozora Library and creates a simple corpus to serve the teaching of Japanese interpretation, which improves the teaching effect. At the same time, this paper discusses the boundaries and difficulties of individual self-built corpora with a view to making breakthroughs and progress in future research and development.

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Section: Related Workmentioning

confidence: 99%

Development and Teaching Application of Japanese Corpus Based on Big Data

Liu

Hua

2022

Wireless Communications and Mobile Computing

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“…Traditional sentiment classification methods mainly include shallow classification methods such as machine-learning-based and lexicon-based methods. The lexicon-based classification algorithms include sentiment lexicon-based methods and corpus-based methods [ 15 ]. For sentiment lexicon-based classification methods, a lexicon must be available first, so before classification, a lexicon must be built first, and the building of a lexicon requires many manual intervention components, which requires a lot of effort.…”

Section: Current Status Of Researchmentioning

confidence: 99%

“…To further illustrate the effectiveness and rationality of the bidirectional training strategy and MLP, first, the results of SNN on the FB15k dataset with and without the bidirectional training strategy are reported [ 15 ]. Also, to demonstrate the usefulness of the bidirectional training strategy, it is applied to TransE [ 16 ].…”

Section: Design Of English Translation Model With Chaotic Neural Network For Big Data Analysismentioning

confidence: 99%

[Retracted] A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis

Cao

Hao²

2021

Computational Intelligence and Neuroscience

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In this paper, the chaotic neural network model of big data analysis is used to conduct in-depth analysis and research on the English translation. Firstly, under the guidance of the translation strategy of text type theory, the translation generated by the machine translation system is edited after translation, and then professionals specializing in computer and translation are invited to confirm the translation. After that, the errors in the translations generated by the machine translation system are classified based on the Double Quantum Filter-Muttahida Quami Movement (DQF-MQM) error type classification framework. Due to the characteristics of the source text as an informative academic text, long and difficult sentences, passive voice, and terminology translation are the main causes of machine translation errors. In view of the rigorous logic of the source text and the fixed language steps, this research proposes corresponding post-translation editing strategies for each type of error. It is suggested that translators should maintain the logic of the source text by converting implicit connections into explicit connections, maintain the academic accuracy of the source text by adding subjects and adjusting the word order to deal with the passive voice, and deal with semitechnical terms by appropriately selecting word meanings in postediting. The errors of machine translation in computer science and technology text abstracts are systematically categorized, and the corresponding post-translation editing strategies are proposed to provide reference suggestions for translators in this field, to improve the quality of machine translation in this field.

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“…In order to study the semantic changes of Korean verbs, Lou et al established a big data analysis method, which studied the characteristics of verbs through the constructed database, data mining, and data analysis, and established a system framework containing a large amount of text corpus. Finally, the semantic changes of Korean verbs were deeply studied [18]. The great ruins are important witnesses of human civilization.…”

Section: Introductionmentioning

confidence: 99%

Cultural Cognition and Analytical Methods of Chinese and Korean Envoys in Ming Dynasty Based on Big Data Analysis Technology

Wang

Tang

2022

Advances in Multimedia

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During the Ming Dynasty, China and Korea exchanged frequently and recorded a large amount of written information, which is of great value for understanding the culture of that time. The large amount of data makes it difficult to conduct quantitative analysis by researchers, which makes the analysis limited. This paper carries out a research on the cognition and analysis method of Chinese and Korean envoys to foreign cultures in Ming Dynasty based on big data analysis technology. Based on the literature research, this paper determines the ontology model establishment method to efficiently detect the written records of Chinese and Korean envoys in Ming Dynasty. The established ontology model and the improved clustering analysis method can improve the efficiency of data detection, reduce the error of data detection, and provide data basis for the research of this paper. According to the technology of big data analysis, this paper analyzes the focus and status class of Chinese and Korean envoys in Ming Dynasty and analyzes cognition of the Chinese and Korean envoys for the foreign culture. The results show that the envoys of the Chinese and Korean pay different attention to the foreign culture due to their different cognition of the foreign culture, compared with Ming Dynasty envoys, Korean envoys paid 15.3 percent less attention to geography, 19.7 percent more to history, 11.7 percent more to people, and 16 percent less to customs. This reflects the two envoys’ different perceptions of the foreign culture. And the status class of the envoys exacerbates this difference. In the early Ming Dynasty, the creative diversity of Ming envoys was far lower than that of Korean envoys. As time went by, the creative diversity of Ming envoys increased. The results provide support for further understanding of Chinese and Korean culture and their relationship in Ming Dynasty.

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Semantic change analysis of Korean verbs based on massive culture corpus data

Cited by 3 publications

References 25 publications

Development and Teaching Application of Japanese Corpus Based on Big Data

Development and Teaching Application of Japanese Corpus Based on Big Data

[Retracted] A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis

Cultural Cognition and Analytical Methods of Chinese and Korean Envoys in Ming Dynasty Based on Big Data Analysis Technology

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