Enhancing information retrieval through advanced query expansion techniques and semantic analysis

Optimizing the speed and precision with which useful information could be retrieved is the primary goal of Information Retrieval Enhancement. The authors discuss their motivation for creating better data retrieval methods in this work. The study investigates the use of semantic analysis and complex Query Expansion (QE) techniques to enhance the quality of search engine results. This research aims to enhance current techniques for retrieving data by using cutting-edge algorithms and semantic analysis tools. This study proposed the novel Bidirectional Encoder Representations from Transformers (BERT) and conditional random fields (CRF) to extract information from the Yahoo Dataset. Contextualized word embeddings with BERT and sequence labelling with CRF improve data analysis and extraction. The experimental results presented in the research demonstrate the effectiveness of these enhanced tactics in producing more precise and relevant search results by intelligently extending query terms and understanding the context of user searches. The results indicate that the proposed method exhibits a high degree of accuracy (99.7%), a substantial rate of recall (98.0%), a considerable rate of precision (99.8%), and a noteworthy value of F1-score (98%). Future research should focus on intelligent search engines that make it easy to access large volumes of data from several sources. These tools would use state-of-the-art algorithms for machine learning (ML) and methods for natural language processing.