The paper introduces an extended Boolean information retrieval system that bridges the gap between traditional Boolean retrieval and vector-processing models. This system retains the query structure of Boolean retrieval while incorporating weighted terms in both queries and documents. The retrieved output is ranked based on the similarity between the query and the document, providing a more nuanced ranking than traditional Boolean systems. The authors discuss the motivation behind the extended model, which aims to address the limitations of both conventional Boolean and vector-processing retrieval systems. They introduce the p-norm model, which allows for varying levels of strictness in interpreting Boolean connectives by adjusting the value of \( p \). The paper also presents experimental results demonstrating the effectiveness of the extended retrieval model, showing significant improvements over both the standard Boolean and vector-processing systems in terms of recall and precision. The evaluation is conducted using four different document collections, and the results highlight the advantages of the extended model in retrieving relevant documents with higher precision and recall.The paper introduces an extended Boolean information retrieval system that bridges the gap between traditional Boolean retrieval and vector-processing models. This system retains the query structure of Boolean retrieval while incorporating weighted terms in both queries and documents. The retrieved output is ranked based on the similarity between the query and the document, providing a more nuanced ranking than traditional Boolean systems. The authors discuss the motivation behind the extended model, which aims to address the limitations of both conventional Boolean and vector-processing retrieval systems. They introduce the p-norm model, which allows for varying levels of strictness in interpreting Boolean connectives by adjusting the value of \( p \). The paper also presents experimental results demonstrating the effectiveness of the extended retrieval model, showing significant improvements over both the standard Boolean and vector-processing systems in terms of recall and precision. The evaluation is conducted using four different document collections, and the results highlight the advantages of the extended model in retrieving relevant documents with higher precision and recall.