How to use AI to check the coherence of sections in a paper?

AI can effectively assess section coherence in papers by analyzing semantic relationships and flow between text segments. NLP algorithms identify conceptual linkages and logical progression. Key principles involve computational linguistics and discourse analysis, utilizing embedding algorithms to represent text meaning and similarity metrics to quantify connection strength. Necessary conditions include text digitization, appropriate training data for domain-specific models, and avoiding overly fragmented sections. Applicable across academic domains, constraints exist in interpreting complex argumentative nuance. Crucially, treat AI as an assistive tool; human review remains essential for contextual and disciplinary coherence validation. Focus primarily on transition patterns and topical continuity rather than grammatical correctness. Implementation involves multiple stages: preprocess text into paragraphs or sections; employ embedding models (BERT, Doc2Vec) to generate vector representations; calculate inter-section similarity via cosine distance or graph-based centrality; analyze lexical cohesion markers like repeated keywords and transition words; visualize flow through network graphs highlighting weak connections. Scenario applications include draft revision phases, literature review structuring, and thesis organization. This approach objectively pinpoints disjunctions, reduces manual review burden, and strengthens overall argumentative integrity. Always cross-validate flagged issues.