Webpage Text Element Translation via Contextual Reordering
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Solution Overview
Problem
Conventional translation tools fail to accurately translate web-based content due to their inability to account for context, often resulting in confusing outputs as they translate word for word or sentence for sentence without considering the domain and hierarchical relationships of text elements on a webpage.
Innovation Solution
A computer-implemented method that determines domain and category information from webpage metadata, calculates word vectors based on grouping relationships and context information, and reorders translations using extracted text feature types to provide contextually accurate translations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional translation tools are used to translate web content word for word or sentence for sentence, then the translation process is simple and fast, but the translation quality is poor and confusing due to lack of context consideration
Solution Approach 1:
The patent segments the webpage into hierarchical components (e.g., header, navigation, main content, footer) and further divides text elements into groups based on their semantic relationships. This segmentation allows the translation system to process and translate different parts of the content with appropriate contextual awareness, resolving the contradiction between simple translation process and high translation quality.
Solution Approach 2:
The patent introduces multiple dimensions of analysis including domain classification, category identification, component type determination, and hierarchical relationships. By adding these dimensional layers to the translation process, the system achieves context-aware translation without simply translating word-for-word, thus improving quality while managing complexity through structured analysis.
2Measurement precision
If word vectors and context information are calculated for all text elements, then translation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies different levels of processing to different text elements based on their importance and contextual relevance. Not all text elements require the same degree of analysis - the system identifies key elements that require full contextual analysis while applying lighter processing to less critical elements, thus improving accuracy where needed without uniformly increasing processing time across all content.
Solution Approach 2:
The system performs preliminary classification of webpages into domains and categories, and pre-establishes hierarchical relationships between text elements before translation occurs. This preliminary organization allows the translation process to leverage pre-computed structural information, reducing the computational burden during actual translation while maintaining high accuracy through context-aware processing.
3Reliability
If text elements are translated without considering domain and hierarchical relationships, then the translation process is straightforward, but the output is confusing and fails to reflect intended meaning
Solution Approach 1:
The patent creates a multi-functional translation system that simultaneously performs domain classification, category identification, component type determination, hierarchical relationship mapping, and translation. By integrating these functions into a single cohesive system, the patent achieves reliable context-aware translation without requiring separate complex systems for each function, thus improving reliability while managing overall system complexity.
Data Source
AI summary
A computer-implemented method, according to one embodiment, includes determining a domain and category information from metadata associated with a first webpage. The method further includes determining component types, hierarchies and grouping relationships for a plurality of text elements on the first webpage and constructing context information for the text elements. Word vectors are calculated based on the grouping relationships and the context information, and text feature types of the text elements are extracted based on the word vectors and the context information. The method further includes using the extracted text feature types to determine a re-ordering of a translation of the text elements. A computer program product, according to another embodiment, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method.