Deep Learning in Natural Language Processing

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.