The growing complexity and volume of technical documentation in engineering, scientific, and computational fields present significant challenges for understanding, especially for non-experts, students, and interdisciplinary stakeholders. Technical documents often detail complex processes, hierarchical system architectures, and interrelated components using dense terminology and structured language [15]. Understanding such materials typically demands considerable cognitive effort, as readers need to mentally create visual models based on textual descriptions. To tackle this issue, this paper introduces an explainable Natural Language Processing (NLP) system aimed at automatically transforming complex technical text into comprehensible visual diagrams. The suggested method incorporates advanced NLP techniques—including semantic parsing, named entity recognition, syntactic dependency analysis, and relation extraction—alongside a structured diagram generation framework. Initially, the system converts unstructured textual input into an intermediate knowledge representation that encapsulates entities, actions, attributes, and their logical connections. This structured representation is then systematically aligned with diagram components such as nodes, edges, and process flows, ensuring that the resulting visual output accurately reflects the procedural and conceptual framework of the original material [2]. Unlike conventional text summarization or visualization tools that focus on brevity or aesthetic appeal, the proposed system prioritizes interpretability, transparency, and traceability. An explainability module allows users to comprehend how specific textual elements affect diagram creation by highlighting extracted keywords, inferred relationships, and reasoning pathways [12]. This level of transparency builds trust in automated outputs and enables users to validate, refine, or amend generated diagrams as needed. To assess the effectiveness of the approach, the system was evaluated on a varied corpus of engineering.
Explainable AI, Natural Language Processing (NLP), Technical Text Visualization, Diagram Generation, Semantic Parsing, Information Extraction, Human-Interpretable Models, Technical Documentation Comprehension.
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