Integration of Traditional Information Retrieval and Machine Learning Technologies in Intelligent Document Search

This article analyzes the AI-Semantic-Search project, which integrates TF-IDF, Naive Bayes classifier, and WordNet semantic network to build an intelligent document search engine. It demonstrates the value of traditional technologies in the modern AI search field and provides a reference for understanding the evolution of AI search systems.

Currently, generative AI and large language models dominate the search field, and traditional information retrieval (IR) and machine learning technologies are often overlooked. The AI-Semantic-Search project provides a unique perspective for combining traditional technologies with modern needs, and has both educational significance and practical reference value.

AI-Semantic-Search is an open-source intelligent document search engine that integrates TF-IDF (text relevance evaluation), Naive Bayes (document classification and probabilistic reasoning), and WordNet (semantic understanding and synonym expansion). It can achieve accurate semantic search without complex deep learning models.

The core of the system lies in technical collaboration: TF-IDF calculates the importance weight of words; Naive Bayes performs probabilistic reasoning to evaluate relevance; WordNet improves recall through lexical semantic relationships (synonyms, hypernyms, etc.). This design is suitable for scenarios requiring result interpretation, and the contribution of each component can be clearly identified.

The system provides practical features: users can upload documents in real time via the Streamlit interface, and the system immediately builds an index; it supports Boolean search syntax (AND/OR/NOT), which is convenient for advanced users to construct complex queries and is suitable for academic research and practical applications.

AI-Semantic-Search has an interpretable ranking mechanism based on rules and statistical methods, which can clearly show the reasons for document ranking. This transparency helps understand algorithm principles and provides guidance for optimization strategies, which is particularly valuable in the context of widespread AI black box problems.

Although the project does not rely on large language models, its design concept offers important insights for GEO practitioners: understanding traditional technology principles can help grasp the underlying logic of modern AI search and formulate more effective optimization strategies; the interpretability method provides ideas for designing transparent content optimization solutions.