Abstract
Natural Language Processing (NLP) constitutes a crucial field in the realm of artificial intelligence, concentrating on the interplay between computers and human language. The goal is to empower machines to understand, interpret, and produce human language, thereby closing the divide between humans and computers. Various techniques have been proposed for NLP tasks, and one such method gaining attention is MOORA. In this paper, we present a comprehensive analysis and performance evaluation of NLP tasks using the MOORA method. The MOORA method, also known as Multi-Objective Optimization by Ratio Analysis, is a multi-criteria decision-making technique used for evaluating and ranking alternatives when faced with multiple criteria simultaneously. Its application in NLP tasks offers a promising approach to handle diverse challenges and improve overall system performance. We begin by discussing the fundamental concepts of NLP, including its subfields, applications, and existing methodologies. Subsequently, the MOORA method's theoretical underpinnings are presented, we conduct experiments on a range of common NLP tasks text summarization, and machine translation. For each task, we define relevant criteria, establish performance metrics, and identify suitable alternatives. The results obtained from the MOORA-based evaluations are compared against traditional NLP approaches, such as rule-based systems, statistical models, and deep learning algorithms. Our findings reveal that the MOORA method excels in handling multiple objectives and criteria, leading to improved accuracy, robustness, and adaptability in NLP tasks. Moreover, we investigate the impact of various parameters and data preprocessing techniques on the MOORA-based NLP models to identify best practices and potential areas for further enhancement. The alternatives are Tool A: OpenNLP, Tool B: SpaCy, Tool C: NLTK, Tool D: Stanford NLP, Tool E: Gensim, Tool F: CoreNLP, Tool G: TextBlob and Tool H: Amazon Comprehend. The evaluation parameters are Accuracy, Speed, Language Support, Sentiment Analysis, Cost, User-Friendliness, Documentation and Community Support. Natural Language Toolkit (NLTK) is got first rank and Tool H: Amazon Comprehend is got lowest rank.