دانلود کتاب یادگیری عمیق برای پردازش زبان طبیعی

Language comes naturally to humans but has historically been hard for computers to grasp. This book addresses the application of recent, cutting-edge deep learning techniques to automated language analysis. In the last decade, deep learning has emerged as the vehicle of the latest wave in artificial intelligence (AI). Results have consistently redefined the state of the art for a plethora of data analysis tasks in a variety of domains. For an increasing number of deep learning algorithms, betterthan- human (human-parity or superhuman) performance has been reported: for instance, speech recognition in noisy conditions and medical diagnosis based on images. Current deep learning–based natural language processing (NLP) outperforms all pre-existing approaches by a large margin. What exactly makes deep learning so suitable for these intricate analysis tasks, in particular language processing? This chapter presents some of the background necessary to answer this question and guides you through a selection of important topics in machine learning for NLP. We first examine a few main approaches to machine learning: the neural perceptron, support vector machines, and memory-based learning. After that, we look at historical developments leading to deep learning and address vector representations: encoding data (notably, textual) with numerical representations suitable for processing by neural networks. Let’s start by discussing a few well-known machine learning–based NLP algorithms in some detail, illustrated with a handful of practical examples to whet your appetite. After that, we present the case for deep learning–based NLP.