Mammadov E.
Master of Science in Computer Engineering (2nd year student)
Azerbaijan State Oil and Industrial University
Baku,Azerbaijan

Abstract
The optimization of query understanding within search engines is a critical endeavor that marries the complexities of Natural Language Processing (NLP) with advanced mathematical models. Central to enhancing search engine performance, this exploration covers the roles of syntactic analysis, part-of-speech tagging, and named entity recognition in dissecting and interpreting the structure of user queries. It further delves into the mathematical theories underlying these NLP techniques, spotlighting the use of probabilistic models such as Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs), alongside the emergence of neural network-based approaches. Through a nuanced examination of these methodologies, the discussion underscores how mathematical approaches to NLP significantly bolster the accuracy and relevance of search engine responses, paving the way for a more intuitive and efficient retrieval of information in the digital age.

 Keywords: Natural Language Processing (NLP), query understanding,probabilistic models,Syntactic Analysis,Part-of-specch Tagging (POS),Named Entity Recognition (NER)