Abstract: The Natural Language Processing (NLP) is a sub domain of Artificial Intelligence (AI). NLP is playing a vital role in AI, which is used to bridge the gap between human and machine. AI is a platform for learning outcomes, in that NLP is used to solve problems like conversion of one human language to another human language. NLP spreads in all domains like Questions generation, Question Answering, Evaluation of descriptive answers and knowledge graph completion. The surge of modern NLP is accredited to the evolution of a simple model, perceptions. Including of perceptions was not just a second order with techniques altogether or boosting, but rather exponential if not asymptotic, with the advent of deep neural networks. The influence of NLP has made modernized advancements into real-world applications, i.e. chatbots conversion, real-time translations, hate speech, or forged news detection. Natural Language Processing is highly influenced on human lives, so it is working on many applications like Translator, Search Autocorrect and Autocomplete, Social Media Monitoring, Hiring and Recruitment Survey Analysis, Targeted Advertising ,Voice Assistants Grammar Checkers, Chatbots, End Notes ,Email Filtering, etc.. In this article, we shall start by exploring some machine learning algorithms to give solutions for NLP approaches. The different algorithms Bayesian Networks, Maximum Entropy, Linear Regression, Logistic Regression, Decision Tree, SVM, Naive Bayes, kNN, K-Means, Random Forest.
Keywords: NLP, AI, Machine Language Algorithms, Translator
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DOI:
10.17148/IJARCCE.2022.11499
