Handbook of Natural Language Processing Second Edition。 自然语言处理好书，传统方法，统计方法。各种方法的论文集合。

This book is intended as a resource for people who are interested in using computers to help process natural language. A "natural language" refers to any language spoken by humans, either currently (e.g., English, Chinese, Spanish) or in the past (e.g., Latin, Greek, Sankrit). “Annotation” refers to the process of adding metadata information to the text in order to augment a computer’s abilities to perform Natural Language Processing (NLP). In particular, we examine how information can be added to natural language text through annotation in order to increase the performance of machine learningalgorithms—computer programs designed to extrapolate rules from the information provided over texts in order to apply those rules to unannotated texts later on.

This book attempts to simplify and present the concepts of deep learning in a very comprehensive manner, with suitable, full-fledged examples of neural network architectures, such as Recurrent Neural Networks (RNNs) and Sequence to Sequence (seq2seq), for Natural Language Processing (NLP) tasks. The book tries to bridge the gap between the theoretical and the applicable. It proceeds from the theoretical to the practical in a progressive manner, first by presenting the fundamentals, followed by the underlying mathematics, and, finally, the implementation of relevant examples. The first three chapters cover the basics of NLP, starting with the most frequently used Python libraries, word vector representation, and then advanced algorithms like neural networks for textual data. The last two chapters focus entirely on implementation, dealing with sophisticated architectures like RNN, Long Short-Term Memory (LSTM) Networks, Seq2seq, etc., using the widely used Python tools TensorFlow and Keras. We have tried our best to follow a progressive approach, combining all the knowledge gathered to move on to building a question- and-answer system. The book offers a good starting point for people who want to get started in deep learning, with a focus on NLP. All the code presented in the book is available on GitHub, in the form of IPython notebooks and scripts, which allows readers to try out these examples and extend them in interesting, personal ways.

Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning By 作者: Delip Rao – Brian McMahan ISBN-10 书号: 1491978236 ISBN-13 书号: 9781491978238 Edition 版本: 1 出版日期: 2019-02-11 pages 页数: (256 ) $89.99 Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations. Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems

Natural Language Processing with PyTorch 2018 Brian McMahan and Delip Rao

CS224d Deep Learning for Natural Language Processing（下）

Foundations of Statistical Natural Language Processing.pdf

Neural Network Methods for Natural Language Processing by Yoav Goldberg，上传的这个为英文版，方便大家看原版。中文版本为车万翔老师翻译的《基于深度学习的自然语言处理》

An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology – at all levels and with all modern technologies – this book takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. KEY TOPICS: Builds each chapter around one or more worked examples demonstrating the main idea of the chapter, using the examples to illustrate the relative strengths and weaknesses of various approaches. Adds coverage of statistical sequence labeling, information extraction, question answering and summarization, advanced topics in speech recognition, speech synthesis. Revises coverage of language modeling, formal grammars, statistical parsing, machine translation, and dialog processing. MARKET: A useful reference for professionals in any of the areas of speech and language processing.

I Preliminaries 1 1 Introduction 3 2 Mathematical Foundations 39 3 Linguistic Essentials 81 4 Corpus-Based Work 117 II Words 149 5 Collocations 151 6 Statistical Inference: n-gram Models over Sparse Data 191 7 Word Sense Disambiguation 229 8 Lexical Acquisition 265 III Grammar 315 9 Markov Models 317 10 Part-of-Speech Tagging 341 11 Probabilistic Context Free Grammars 12 Probabilistic Parsing 407 381 I v Applications and Techniques 461 13 Statistical Alignment and Machine Translation 14 Clustering 495 15 Topics in Information Retrieval 529 16 Text Categorization 575