Automated Text Meaning Detection via Numeric Signal Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies face challenges in automatically determining the univocal meaning of words in sentences, particularly with homonyms and homophones, as existing methods rely on human interpretation or statistical/graph-based approaches, leading to inaccurate translations and search results.
Innovation Solution
The method involves assigning unique numeric meaning-signals to words, which are then combined and analyzed contextually to determine the relevant meaning, allowing for automatic identification of univocal sentences and accurate translation and search processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If statistical or graph-based approaches are used to determine word meanings, then automation is improved, but measurement precision deteriorates due to inaccurate translations and search results
Solution Approach 1:
The patent replaces statistical and graph-based computational approaches with a semantic field theory-based system. Meaning signals are represented as vectors in a semantic space, and word meanings are determined through geometric relationships (cosine similarity, vector operations) rather than statistical probability or graph traversal. This substitution maintains automation while improving precision by using mathematically rigorous semantic measurements.
2Manufacturing precision
If context-controlled comparison of meaning-signals is performed, then manufacturing precision is improved, but device complexity increases due to arithmetic and logical combination processes
Solution Approach 1:
The patent transforms semantic meaning determination into a parameter-based computational problem. Each word is assigned a meaning signal vector with specific parameters (dimensions representing semantic features). Contextual meaning is determined by comparing these parameter vectors through arithmetic operations (addition, subtraction, scalar multiplication) and logical operations (threshold comparisons, similarity calculations). This parameterization enables precise control while maintaining manageable computational complexity through standardized mathematical operations.
Data Source
AI summary
A method for automatically detecting meaning patterns in a text that includes input words of at least one sentence, includes a database system containing words of a language, a number of defined categories of meaning in order to describe properties of the words, and meaning signals for all the words stored in the database, wherein a meaning signal is a clear numerical characterization of the meaning of the word using the categories of meaning.