<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper introduces a novel method combining semantic contrast learning and event detection, employing an attention mechanism to compare and analyze semantic features between two distinct text types. We utilize a convolutional neural network to extract deep semantic information from texts, while the attention mechanism models global dependencies to elucidate complex semantic information. In event detection, we construct positive and negative samples using event-type labeling information, facilitating the differentiation of semantic spaces associated with various event types in text. We apply this methodology to a corpus relevant to vocational education English translation, aiming to extract and analyze stylistic features. Our results reveal distinct lexical characteristics; the Type Token Ratio (TTR) and Standardized Type Token Ratio (STTR) for the Statements are calculated at 25 and 45.08, respectively. In the analysis of high-frequency words within the English translation of the Report, nouns and adjectives are predominant, with counts of 4,466 and 1,962, respectively, surpassing those in the English translation of the Consultation. Syntactic feature analysis indicates that the overall trends in both text types are consistent; however, there is a notable variation in sentence length deviation, which increases from 1.47 to 3.36 in the Report and decreases from 2.49 to 2.31 in the Consultation. This differential analysis underscores the nuanced stylistic adaptations between the two translated text types.</p>
</abstract>

This paper introduces a novel method combining semantic contrast learning and event detection, employing an attention mechanism to compare and analyze semantic features between two distinct text types. We utilize a convolutional neural network to extract deep semantic information from texts, while the attention mechanism models global dependencies to elucidate complex semantic information. In event detection, we construct positive and negative samples using event-type labeling information, facilitating the differentiation of semantic spaces associated with various event types in text. We apply this methodology to a corpus relevant to vocational education English translation, aiming to extract and analyze stylistic features. Our results reveal distinct lexical characteristics; the Type Token Ratio (TTR) and Standardized Type Token Ratio (STTR) for the Statements are calculated at 25 and 45.08, respectively. In the analysis of high-frequency words within the English translation of the Report, nouns and adjectives are predominant, with counts of 4,466 and 1,962, respectively, surpassing those in the English translation of the Consultation. Syntactic feature analysis indicates that the overall trends in both text types are consistent; however, there is a notable variation in sentence length deviation, which increases from 1.47 to 3.36 in the Report and decreases from 2.49 to 2.31 in the Consultation. This differential analysis underscores the nuanced stylistic adaptations between the two translated text types.

Analysis of Corpus Analysis of English Translation in Vocational Education Based on Semantic Contrastive Analysis

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper introduces a novel method combining semantic contrast learning and event detection, employing an attention mechanism to compare and analyze semantic...

Analysis of Corpus Analysis of English Translation in Vocational Education Based on Semantic Contrastive Analysis

Publicado en línea: 10 jun 2024

Páginas: -

Recibido: 25 feb 2024

Aceptado: 28 abr 2024

DOI: https://doi.org/10.2478/amns-2024-1472

Palabras clave
Comparative learning, Event detection, English translation, Semantic features

© 2024 Xia Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of Corpus Analysis of English Translation in Vocational Education Based on Semantic Contrastive Analysis

Publicado en línea: 10 jun 2024

Páginas: -

Recibido: 25 feb 2024

Aceptado: 28 abr 2024

DOI: https://doi.org/10.2478/amns-2024-1472

Palabras claveComparative learning, Event detection, English translation, Semantic features

© 2024 Xia Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Comparative learning, Event detection, English translation, Semantic features