How to check the usage of terms in a paper through AI?

AI technology enables systematic verification of terminology usage in academic papers through computational linguistics and pattern recognition algorithms. This automated approach identifies frequency, consistency, and contextual application of specific terms within a manuscript. Key principles involve natural language processing (NLP) techniques like tokenization and named entity recognition to isolate and classify terms. Necessary conditions include digital text access and predefined term glossaries or taxonomies. The system's scope covers detecting overuse, inconsistent definitions, or contextually inappropriate applications across sections. Core precautions entail validating AI outputs against domain expertise due to potential nuances in academic discourse and mitigating false positives from polysemous terms. Implementation involves uploading the manuscript to specialized AI-powered text analysis platforms. Users specify target terms or enable automated terminology extraction. The software generates quantitative metrics (e.g., term frequency distributions) and qualitative assessments (e.g., contextual usage examples across sections). Researchers review these outputs to align terminology with disciplinary standards and editorial guidelines, enhancing precision during revisions or collaborative writing processes.