Search Engine Using NLP Text Processing Techniques to Extract Most Relevant Search Results

This research proposes the use of a machine learning-driven search engine that employs natural language processing techniques to enhance search results in the face of expanding digital data. To achieve this, the search engine relies on a diverse corpus of relevant text data, which undergoes pre-processing. By extracting features such as word frequency, TF-IDF, or semantic embeddings like Word2Vec or GloVe, the search engine can identify word patterns and relationships, thus improving the accuracy of its language model. The trained model is then evaluated using benchmark or annotated data to identify weaknesses and make improvements. The study emphasizes the potential of optimizing ML-based search engines for specific domains by training algorithms on domain-specific data and incorporating specialized features and knowledge. Overall, this approach offers a promising solution for effectively managing and analyzing the ever-increasing volume of digital data.