How can AI technology be used to conduct intelligent analysis of literature?

AI technology enables the systematic and scalable extraction of meaningful patterns, trends, and insights from large volumes of scholarly literature through automated natural language processing and machine learning techniques. This facilitates efficient knowledge discovery. Effective intelligent literature analysis relies on core natural language processing methods like named entity recognition, relationship extraction, topic modeling, and sentiment analysis. Key requirements include access to comprehensive, machine-readable text corpora and appropriate computational resources. The approach excels at processing vast datasets beyond human capacity, identifying emerging themes, mapping conceptual networks, and uncovering hidden connections. However, results necessitate human expertise for validation, contextual interpretation, and bias mitigation due to potential limitations in model training data and algorithmic nuance. Implementation typically involves acquiring relevant digital literature, preprocessing text for analysis, selecting and applying suitable AI models (e.g., topic models for theme discovery, transformers for summarization), visualizing results, and critically interpreting findings. This supports systematic reviews, research trend identification, gap analysis, and academic monitoring, significantly accelerating literature synthesis and enhancing research strategy development.