Lexical Ambiguity Resolution in Automatic Translation
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Solution Overview
Problem
Automatic translation systems struggle to accurately translate word sequences due to lexical ambiguity, leading to linguistic misunderstandings, especially in sensitive fields like technical documentation, as they cannot discern context like human translators can.
Innovation Solution
A method and data processing device that analyze word sequences for terms with lexical ambiguity using a terminology database, assign term identifiers based on context, and translate these terms into the target language to ensure accurate and unambiguous translations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic translation is used without context analysis, then processing speed is improved, but translation accuracy deteriorates due to lexical ambiguity
Solution Approach 1:
The translation process is segmented into distinct phases: initial automatic translation for speed, followed by separate context analysis phase, and finally a resolution phase for ambiguous terms. This segmentation allows the system to maintain high processing speed while systematically addressing accuracy issues through targeted context analysis.
Solution Approach 2:
A context database is introduced as an intermediary component between the source text and translation output. This database stores contextual information about terms and their usage, enabling the system to resolve lexical ambiguity by querying contextual meanings rather than relying solely on direct translation, thus improving accuracy without sacrificing overall processing efficiency.
2Reliability
If context analysis is performed to resolve lexical ambiguity, then translation accuracy is improved, but device complexity increases
Solution Approach 1:
Contextual information about terms is pre-analyzed and stored in a context database before the actual translation process. This preliminary action creates a ready-reference knowledge base that the translation system can query efficiently, avoiding the need for complex real-time context analysis during translation and thus reducing system complexity while maintaining high accuracy.
Solution Approach 2:
The system uses its own accumulated contextual knowledge from previous translations and analyses to resolve ambiguities autonomously. By leveraging its internal context database, the translation system can self-resolve lexical ambiguities without requiring external human intervention or overly complex external systems, thereby managing complexity while improving accuracy.
3Measurement precision
If term identifiers are assigned based on multiple meanings, then translation precision is improved, but information processing complexity increases
Solution Approach 1:
Instead of applying a uniform complex identification system to all terms, the patent assigns term identifiers with multiple meanings only to specific ambiguous terms where needed. This localized approach applies enhanced precision only where lexical ambiguity exists, while leaving common unambiguous terms to be processed through simpler pathways, thus improving overall precision without proportionally increasing processing complexity.
Data Source
AI summary
A method and a data processing device are disclosed for at least partially automatically transferring a word sequence composed in a source language into a word sequence in a target language with corresponding substantive content. By analyzing the word sequence and identifying terms with lexical ambiguity in the word sequence by comparing with a terminology database comprising terms with lexical ambiguity in the source language which are assigned a plurality of term identifiers depending on their number of meanings, an unambiguous term definition is provided for translating the word sequence into the target language by assigning a term identifier to the term with lexical ambiguity in the source language. This may render a machine translation less susceptible to errors.