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》

一篇非常有用的自然场景下文本识别的论文,源代码是用C++写的

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

Natural Language Processing with Python自然语言处理中文版。作为一个自然语言处理的初学者，看书看到“训练模型”，这模型那模型的，一直不知 道模型究竟是什么东西。看了这本书，从预处理数据到提取特征集，训练模型，测试修改等， 一步一步实际操作了之后，才对模型一词有了直观的认识（算法的中间结果，存储在计算机 中的一个个 pkl 文件，测试的时候直接用，前面计算过的就省了）。以后听人谈“模型”的 时候也有了底气。当然，模型还有很多其他含义。还有动词的“配价”、各种搭配、客观逻 辑对根据文法生成的句子的约束如何实现？不上机动手做做，很难真正领悟。 自然语言处理理论书籍很多，讲实际操作的不多，能讲的这么系统的更少。从这个角度 讲，本书是目前世界上最好的自然语言处理实践教程。初学者若在看过理论之后能精读本书， 必定会有获益。这也是翻译本书的目的之一。