Publication

Abstract : Natural language processing (NLP) has witnessed significant advancements in recent decades. Automatically classifying parts of speech, like nouns and verbs, from textual input has transformed text analysis and language understanding. Using natural language processing techniques, we explore various methods for identifying noun and verb phrases automatically, with an emphasis on high accuracy. Our study explores rule-based, statistical, and Machine Learning (ML) approaches for determining the nouns and verbs from sentences. The effectiveness of these approaches is clearly evident, especially when NLP libraries such as SpaCy and the Natural Language Toolkit (NLTK) are used. As well as demonstrating their potential applications across diverse language processing tasks and industries, we conduct comparative research to showcase their advantages and disadvantages. The performance of these methods is also examined in terms of retrieving subject and action terms. SpaCy achieves an impressive accuracy of 95% in noun and verb extraction, while Part-Of-Speech (POS) technology tagging delivers an even higher accuracy of 96%. The results obtained with these methods illustrate how nouns, verbs, and names can be classified in text successfully.