DESIGN AND DEVELOPMENT OF MATHEMATICAL MODELS FOR NATURAL LANGUAGE PROCESSING

Computers are now able to comprehend and analyse human language thanks in large part to natural language processing (NLP). The intricate structure and semantics of natural language can only be understood and analysed with the use of mathematical models. In this study, mathematical models for NLP are designed and developed, with a particular emphasis on sentiment analysis, syntactic parsing, and semantic representation. The first section of the essay examines syntactic parsing, which entails disassembling phrases into their component elements and figuring out how they relate to one another. We address more modern approaches like neural network-based parsers as well as more established methods like dependency parsing and context-free grammars. To accomplish correct parsing, these models' design and implementation make use of graph theory, probabilistic modelling, and machine learning methods. A critical NLP task that seeks to identify the sentiment or emotion portrayed in text. With a focus on methods like support vector machines, recurrent neural networks, and deep learning architectures, we study both rule-based and machine learning-based approaches for sentiment analysis. These models employ methods like feature engineering, sentiment lexicons, and attention mechanisms and are trained on substantial annotated datasets. Throughout the paper, we stress the value of mathematical modelling for NLP and how it helps us deal with the complexity and ambiguity that come with natural language. We go over issues like data scarcity, domain adaption, and computational effectiveness that arise when constructing these models