Text Reading Level Adjustment via Semantic Synonym Substitution
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Solution Overview
Problem
Existing text adjustment systems fail to provide incremental reading level adjustments that align with natural learning progress, often replacing structural words without considering semantics or context, leading to nonsensical sentences and inadequate learning outcomes.
Innovation Solution
The technology adjusts the reading level of text by substituting words with synonyms that take into account context and semantics, focusing on parts of speech like adjectives, verbs, and nouns, and uses a heuristic to select complexity levels that incrementally increase difficulty, ensuring grammatical correctness and meaningful substitutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If text reading level is adjusted by substituting words with synonyms, then reading level can be increased or decreased, but the substitutions may replace structural words without considering semantics or context, leading to nonsensical sentences
Solution Approach 1:
The patent applies local quality by differentiating between structural words and content words in text. Structural words (conjunctions, prepositions, articles, etc.) are excluded from synonym substitution while content words (nouns, verbs, adjectives) are targeted for substitution. This selective approach maintains sentence semantics and grammatical correctness while still achieving reading level adjustment through substitution of appropriate content words with synonyms of varying complexity.
Solution Approach 2:
The patent introduces an intermediary mechanism - a part-of-speech tagger and synonym selector system - that acts between the original text and the adjusted text. This intermediary analyzes the grammatical structure and semantic context of each word, determines whether substitution is appropriate, and selects suitable synonyms that maintain sentence meaning. This intermediary layer prevents nonsensical substitutions while enabling effective reading level adjustment.
2Productivity
If text difficulty is increased significantly to challenge students, then learning opportunity is optimized, but students may become discouraged when presented with text above their reading level
Solution Approach 1:
The patent applies dynamics by making the text difficulty adjustable and adaptable to individual student reading levels. Instead of using fixed difficulty levels, the system dynamically adjusts text complexity by substituting words with synonyms at appropriate complexity levels. This allows text to be tailored to each student's current reading ability while providing incremental challenge, maintaining both learning efficiency and student engagement.
Solution Approach 2:
The patent changes the parameter of word complexity through synonym substitution. By selecting synonyms with different complexity levels (e.g., simpler or more advanced vocabulary), the system can precisely adjust text difficulty to match student reading levels or provide incremental challenges. This parameter change approach enables fine-grained control over text difficulty, optimizing both learning efficiency and student engagement.
3Manufacturing precision
If multiple words are substituted to make a large change in reading level, then control over reading level is improved, but the process becomes more complex and may affect text quality
Solution Approach 1:
The patent applies segmentation by breaking down the text processing task into distinct stages: part-of-speech tagging, synonym identification, substitution selection, and text generation. This segmentation allows the system to process multiple word substitutions systematically while maintaining text quality. Each stage handles a specific aspect of the substitution process, making the overall complex task manageable and controllable.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system evaluates the impact of substitutions on reading level and text quality. By monitoring changes in reading level metrics and assessing substitution appropriateness, the system can adjust its substitution strategy to achieve desired reading level changes while maintaining text quality. This feedback loop enables precise control over reading level adjustment even when multiple words are substituted.
Data Source
AI summary
The technology described herein helps a student learn how to read by providing text at a reading level suitable for learning. The technology described herein can take an input text and adjust the reading level to match the target reading level. The text can be adjusted through word substitution. Prior to outputting the complexified text, a grammar check can determine that the substitution did not generate a grammatically incorrect complexified text.