An english text context adaptation method for cross-cultural communication

By segmenting, parsing, and matching English text with a cultural knowledge base, expanding the context to obtain contextual fragments and performing sentiment analysis, the problem of accuracy in identifying cultural meanings in English texts is solved, thus improving the accuracy and depth of cross-cultural communication.

CN122154679APending Publication Date: 2026-06-05HARBIN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HARBIN UNIV
Filing Date
2026-02-05
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing methods for analyzing the context of English texts struggle to accurately capture the complete connection between surface linguistic forms and deeper cultural meanings, leading to frequent misunderstandings in cross-cultural communication, especially in scenarios such as international business and multinational team collaboration.

Method used

By segmenting and parsing English texts to obtain linguistic features of idioms and euphemisms, similarity matching is performed using a cultural knowledge base, contextual fragments are expanded to obtain contextual information, and sentiment analysis is conducted. Sentiment bias annotation is then integrated to construct an enhanced contextual framework.

Benefits of technology

It achieves precise labeling from linguistic features to cultural affiliation judgment and emotional inclination, improving the accuracy and depth of cross-cultural communication, and is suitable for intelligent translation and culturally adaptive interaction in multilingual environments.

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Abstract

The application provides an English text context self-adaption method for cross-cultural communication, comprising: performing word segmentation and grammar analysis on the English text to obtain a preliminary language feature vector; performing similarity matching on the preliminary language feature vector and a cultural knowledge base to determine a cultural attribution label of the preliminary language feature vector; if the cultural attribution label corresponds to multiple cultural meanings, obtaining surrounding sentence information through English text context to obtain an extended context segment; performing emotion analysis on the extended context segment in combination with the cultural attribution label to obtain an emotion tendency label, and filling in missing cultural details after fusion processing of the emotion tendency label and the preliminary language feature vector to obtain an enhanced context framework, completing cross-cultural context representation, realizing dynamic analysis of cultural meanings and accurate labeling of emotion tendencies, significantly improving the accuracy and depth of language understanding in cross-cultural communication, and being suitable for intelligent translation and cultural adaptive interaction in a multilingual environment.
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