TRILANG MATH SOLVER: A MULTILINGUAL MATHEMATICAL APPLICATION FOR YORUBA, ENGLISH AND FRENCH
Abstract
ABSTRACT
This project explores the application of Natural Language Processing (NLP) techniques in developing an intelligent system capable of understanding and analyzing human language. The primary objective is to design and implement a model that can process unstructured text data to extract meaningful insights and support automated decision-making.
Using techniques such as tokenization, lemmatization, sentiment analysis, and named entity recognition, the system processes large volumes of textual data obtained from online sources and user-generated content.
The system was evaluated based on its accuracy, speed, and efficiency in understanding language patterns and interpreting context. Results show that the NLP model can effectively classify sentiments, identify key entities, and summarize large texts with high reliability.
This work demonstrates the transformative potential of NLP in fields such as customer feedback analysis, social media monitoring, and intelligent virtual assistants. It contributes to the growing body of research aimed at bridging the gap between human language and machine understanding.
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