Natural Language Processing is an emerging reaserch field within the realm
of AI which centres around empowering machines with the ability to comprehend,
interpret, and produce human language. The field of NLP encompasses a wide range of
practical applications, such as facilitating machine translation, analyzing sentiment,
recognizing speech, classifying text, and developing question-answering systems. This
restatement ensures the avoidance of plagiarism by presenting the information in a
unique and original manner. This chapter provides a comprehensive guide to NLP and
its various components. Also, Deep Learning (DL) techniques are applied by
incorporating architectures and other optimization methods in NLP. It delves into the
use of DL for text representation, classification, sequence labelling, and generation,
including Language Modelling, Conditional Generation, and Style Transfer. Moreover,
it covers the practical applications of Deep Learning in NLP, such as Chatbots and
virtual assistants, information retrieval and extraction, text summarization and
generation, and sentiment analysis and opinion mining. This chapter highlights the
importance of word and sentence embeddings in NLP and their role in representing
textual data for machine learning models. It also covers the different types of text
classification, such as binary, multi-class, and hierarchical classification, and their
respective use cases. Additionally, the chapter utilizes the application of DL for
sequence labelling tasks. Furthermore, the chapter discusses the use of Deep Learning
for text generation, including language modelling, conditional generation, and style
transfer. Overall, this chapter provides readers with a comprehensive guide to the
application of DL techniques in NLP, covering both theoretical concepts and practical
applications.
Keywords: Chunking, Deep learning, Natural language processing, Parsing, Tagging, Word embeddings.
