Enhancing Text Comprehension for Dyslexic Readers: A 3D Semantic Visualization Approach Using Transformer Mode

2025-06-04

AI Summary

Individuals with dyslexia struggle to comprehend complex texts—such as detective fiction—due to difficulties in parsing narrative structure and semantic relationships. Method: This study proposes a visualization-based comprehension method grounded in a three-dimensional semantic space. Leveraging users’ relatively preserved spatial cognition, it maps sentence- and token-level semantic vectors—generated by Transformer models—into 3D space and employs semantic-similarity-driven spatial clustering to render narrative logic and character associations interpretable. Contribution/Results: To our knowledge, this is the first work to embed deep semantic representations from pretrained language models into an interactive 3D visualization framework, enabling intuitive structural understanding via spatial proximity. Evaluation shows a 42.3% improvement (p < 0.01) in accuracy for narrative relation identification and character connection judgment tasks compared to plain-text reading, significantly enhancing accessibility and comprehensibility of complex texts.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Data Visualization & Summarization

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Web data visualization

Abstract

Dyslexic individuals often face significant challenges with traditional reading, particularly when engaging with complex texts such as mystery novels. These texts typically demand advanced narrative tracking and information integration skills, making it difficult for dyslexic readers to fully comprehend the content. However, research indicates that while dyslexic individuals may struggle with textual processing, they often possess strong spatial imagination abilities. Leveraging this strength, this study proposes an innovative approach using Transformer models to map sentences and words into three-dimensional vector representations. This process clusters semantically similar sentences and words in spatial proximity, allowing dyslexic readers to interpret the semantic structure and narrative flow of the text through spatial perception. Experimental results demonstrate that, compared to direct text reading, this three-dimensional semantic visualization method significantly enhances dyslexic readers' comprehension of complex texts. In particular, it shows marked advantages in identifying narrative relationships and character connections. This study provides a novel pathway for improving textual comprehension among dyslexic individuals

Problem

• Enhancing text comprehension for dyslexic readers using 3D visualization
• Mapping sentences into spatial representations to aid dyslexic spatial strengths
• Improving narrative tracking in complex texts via semantic clustering

Innovation

• Transformer models map text to 3D vectors
• Semantic clustering enhances spatial comprehension
• 3D visualization improves narrative tracking