دانلود کتاب Formal Analysis for Natural Language Processing: A Handbook

The field of Natural Language Processing (NLP) is one of the most important and useful application areas of Artificial Intelligence (AI). NLP is now rapidly evolving, as new methods and toolsets converge with an ever-expanding wealth of available data. This state-of-the-art handbook addresses all aspects of formal analysis for Natural Language Processing. Following a review of the field’s history, it systematically introduces readers to the rule-based model, statistical model, neural network model, and pre-training model in Natural Language Processing.

At a time characterized by the steady and vigorous growth of Natural Language Processing, this handbook provides a highly accessible introduction and much-needed reference guide to both the theory and method of NLP. It can be used for individual study, as the textbook for courses on Natural Language Processing or computational linguistics, or as a supplement to courses on Artificial Intelligence, and offers a valuable asset for researchers, practitioners, lecturers, graduate and undergraduate students alike.

Having been widely used in NLP in recent years, neural networks and Deep Learning have gradually become the mainstream technology in NLP research. Therefore, this chapter will present some details about models based on neural networks and Deep Learning, including the evolution of neural networks, neural networks of our brain, artificial neural networks, Machine Learning, Deep Learning, word vectors, word embedding, dense word vectors, perceptrons, feedforward neural networks, convolutional neural networks, recurrent neural networks, attention mechanisms, external memory, and pretrained models (such as Transformer and BERT).