Feature Extraction of English Semantic Translation Relying on Graph Regular Knowledge Recognition Algorithm

Yang, Lidong

doi:10.31449/inf.v47i8.4901

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…The Lexical Knowledge Spectrum Map is a conceptual tool that illustrates the breadth and depth of an individual's vocabulary and language comprehension [1]. At one end of the spectrum are basic words and concepts commonly understood by the general population, while at the other end lie specialized terms and intricate language constructs typically utilized in academic, technical, or professional settings [2].…”

Section: Introductionmentioning

confidence: 99%

Translation Algorithm of Proper Nouns in English-Chinese Translation Based on Lexical Knowledge Spectrum Map

Song

2024

jes

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The Lexical Knowledge Spectrum Map (LKSM) represents a comprehensive visual representation of the breadth and depth of lexical knowledge within a specific domain or language. By categorizing words and phrases along a spectrum ranging from basic to advanced levels of complexity, the LKSM provides learners and educators with a clear overview of vocabulary proficiency and progression. This dynamic tool not only helps individuals track their language learning journey but also guides instructional planning by identifying areas of focus and potential gaps in lexical understanding. With its intuitive interface and adaptable framework, the LKSM serves as a valuable resource for promoting effective vocabulary acquisition and mastery across diverse linguistic contexts and educational settings. This paper introduces a novel translation algorithm designed specifically for handling proper nouns in English-Chinese translation, leveraging the Lexical Knowledge Spectrum Map (LKSM) and Feature Vector Optimization with Statistical (FVOS) techniques. Proper nouns pose a unique challenge in translation due to their cultural and contextual significance, often requiring specialized handling to ensure accuracy and coherence in the target language. The proposed algorithm utilizes the LKSM to categorize proper nouns along a spectrum of lexical complexity, providing a comprehensive framework for understanding and translating these entities effectively. Additionally, FVOS techniques are employed to optimize feature vectors for proper noun translation, enhancing the algorithm's ability to capture and preserve semantic nuances across languages. With FVOS model English proper nouns translated into Chinese, the proposed algorithm achieves an average accuracy of 85%, outperforming baseline translation methods by 15%. Moreover, specific proper noun categories exhibit notable improvements, with names of people achieving an accuracy of 90%, followed by locations at 85%, and organizations at 80%.

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Section: Introductionmentioning

confidence: 99%

Translation Algorithm of Proper Nouns in English-Chinese Translation Based on Lexical Knowledge Spectrum Map

Song

2024

jes

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Research on Feature Extraction and Optimization Measures of University English Translation for Machine Learning

2024

Applied Mathematics and Nonlinear Sciences

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In the background of increasing translation content, it is no longer possible to rely solely on human translation to solve the problem of cross-language communication, and thus machine translation technology has gradually become an important means to solve the language barrier. In this paper, the semantic content features are extracted from university English using a semantic model based on fuzzy semantic mapping relations. Optimize the model feature extraction based on concept set context matching, introduce Super-Concept and Sub-Concept, calculate the concept semantic translation similarity, and add them into the translation decoding to get the translation optimization results. In this way, the university’s English machine translation system is constructed and evaluated. The maximum values of data recall and context matching rate of the system in this paper are 71.1% and 99.6%, respectively, and the BLEU values of this paper’s model are higher than those of the CNN model and the Transformer machine translation model in the tests of different slicing granularity. The system has high data recall, context matching rate, and translation accuracy, which is feasible and practical in university English translation, and lays a research foundation for further optimization of university English machine translation.

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Feature Extraction of English Semantic Translation Relying on Graph Regular Knowledge Recognition Algorithm

Cited by 2 publications

References 30 publications

Translation Algorithm of Proper Nouns in English-Chinese Translation Based on Lexical Knowledge Spectrum Map

Translation Algorithm of Proper Nouns in English-Chinese Translation Based on Lexical Knowledge Spectrum Map

Research on Feature Extraction and Optimization Measures of University English Translation for Machine Learning

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