Natural Language Processing, also called as NLP, is a fast-growing arena
that comprises the development of algorithms and models to make it possible for
machines to comprehend, translate, and develop human language. There are several
uses for NLP, including automatic translation, sentiment analysis, text summarization,
and speech recognition, and chatbot development. This chapter presents an overview of
learning techniques used in NLP, including supervised, unsupervised, and
reinforcement learning methods coming under machine learning. The chapter also
discusses several popular learning techniques in NLP, such as Support Vector
Machines (SVM) and Bayesian Networks, which are usually helpful in text
classification, Neural Networks, and Deep Learning Models, which also incorporate
Transformers, Recurrent Neural Networks, and Convolutional Neural Networks. It also
covers traditional techniques such as Hidden Markov, N-gram, and Probabilistic
Graphical Models. Some recent advancements in NLP, such as Transfer Learning,
Domain Adaptation, and Multi-Task Learning, are also considered. Moreover, the
chapter focuses on challenges and considerations in NLP learning techniques, including
data pre-processing, feature extraction, model evaluation, and dealing with limited data
and domain-specific challenges.
Keywords: And Support Vector Machines, Bayesian Networks, Convolutional Neural Networks, Multi-Task Learning, Recurrent Neural Networks, Transfer Learning.
