自然语言处理与计算语言学 Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of 自然语言处理与计算语言学 book. This book definitely worth reading, it is an incredibly well-written.
自然语言处理与计算语言学 by Posts & Telecom Press,Bhargav Srinivasa-Desikan Pdf
Python开源社区资深供稿人撰写 文本分析实用指南 计算语言学领域为数不多的作品之一 技术实用性强 侧重于技术细节的实现 提供源码下载 Key Features 平衡了理论与实战案例之间的关系 在掌握理论知识的同时,执运行自己的自然语言处理项目 Book Description自然语言处理是一门融语言学、计算机科学、数学于一体的科学,研究人与计算机之间用自然语言进行有效通信的各种理论和方法。计算语言学是指通过建立形式化的数学模型来分析、处理自然语言,并在计算机上用程序来实现分析和处理的过程,旨在以机器来模拟人的部分或全部语言能力的目的。 本书作为一本借助于Python编程语言以及各种开源工具(如Gensim、spaCy等)来执行文本分析、自然语言处理和计算语言学算法的图书,从应用层面介绍了相关的理论知识和所涉及的技术。《自然语言处理与计算语言学》共分为15章,其内容涵盖了文本分析的定义、使用Python进行文本分析的技巧、spaCy语言模型、Gensim工具、词性标注及其应用、NER标注及其应用、依存分析、主题模型、高级主题建模、文本聚类和文本分类、查询词相似度计算和文本摘要、词嵌入、使用深度学习处理文本、使用Keras和spaCy进行深度学习、情感分析与聊天机器人的原理介绍等。 本书很好地平衡了理论与实战案例之间的关系,因此你可以在掌握理论知识的同时,执运行自己的自然语言处理项目。你将发现Python这一自然语言处理工具所具有的丰富的生态系统,并将进入现代文本分析的有趣世界。 本书适合对自然语言处理的实现细节感兴趣的Python程序开发人员阅读。如果读者具备统计学的基本知识,对学习本书内容会大有裨益。What you will learn 如何使用合适的工具来标注、解析和建模文本 掌握相应框架工具的使用知识 何时为主题模型选择Gensim这样的工具 何时使用Keras进行深度学习。 Who this book is for 本书适合对自然语言处理的实现细节感兴趣的程序开发人员阅读。
Natural Language Processing and Computational Linguistics by Mohamed Zakaria Kurdi Pdf
Natural language processing (NLP) is a scientific discipline which is found at the interface of computer science, artificial intelligence and cognitive psychology. Providing an overview of international work in this interdisciplinary field, this book gives the reader a panoramic view of both early and current research in NLP. Carefully chosen multilingual examples present the state of the art of a mature field which is in a constant state of evolution. In four chapters, this book presents the fundamental concepts of phonetics and phonology and the two most important applications in the field of speech processing: recognition and synthesis. Also presented are the fundamental concepts of corpus linguistics and the basic concepts of morphology and its NLP applications such as stemming and part of speech tagging. The fundamental notions and the most important syntactic theories are presented, as well as the different approaches to syntactic parsing with reference to cognitive models, algorithms and computer applications.
Python和NLTK实现自然语言处理 by Posts & Telecom Press,Nitin Hardeniya Pdf
使用Python实现NLP项目的大量实践经验 Key Features NLTK是自然语言处理领域中非常受欢迎和广泛使用的Python库。 Book DescriptionNLTK是自然语言处理领域中非常受欢迎和广泛使用的Python库。NLTK的优点在于其简单性,其中大多数复杂的自然语言处理任务使用几行代码即可完成。本书旨在讲述如何用Python和NLTK解决各种自然语言处理任务并开发机器学习方面的应用。本书介绍了NLTK的基本模块,讲述了采用NLTK实现自然语言处理的大量技巧,讨论了一些文本处理方法和语言处理技术,展示了使用Python实现NLP项目的大量实践经验。本书主要内容包括文本挖掘/NLP任务中所需的所有预处理步骤,如何使用Python 3的NLTK 3进行文本处理,如何通过Python开展NLP项目。 本书适合NLP和机器学习领域的爱好者、Python程序员以及机器学习领域的研究人员阅读。 本书包括以下主要内容 • 使用词性标注和分块的方法对文本进行整理与清洗。 • 把文本标记为句子,把句子标记为单词。 • 文本分类和情感分析。 • 字符串匹配算法和归一化方法。 • 信息检索和文本摘要的概念。 • 使用Python解决各种NLP任务的方式。What you will learn 使用词性标注和分块的方法对文本进行整理与清洗。 把文本标记为句子,把句子标记为单词。 文本分类和情感分析。 字符串匹配算法和归一化方法。 信息检索和文本摘要的概念。 使用Python解决各种NLP任务的方式。 Who this book is for 本书适合NLP和机器学习领域的爱好者、对文本处理感兴趣的读者、想要快速学习NLTK的资深Python程序员以及机器学习领域的研究人员阅读。
Linguistic Fundamentals for Natural Language Processing by Emily M. Bender Pdf
Many NLP tasks have at their core a subtask of extracting the dependencies—who did what to whom—from natural language sentences. This task can be understood as the inverse of the problem solved in different ways by diverse human languages, namely, how to indicate the relationship between different parts of a sentence. Understanding how languages solve the problem can be extremely useful in both feature design and error analysis in the application of machine learning to NLP. Likewise, understanding cross-linguistic variation can be important for the design of MT systems and other multilingual applications. The purpose of this book is to present in a succinct and accessible fashion information about the morphological and syntactic structure of human languages that can be useful in creating more linguistically sophisticated, more language-independent, and thus more successful NLP systems. Table of Contents: Acknowledgments / Introduction/motivation / Morphology: Introduction / Morphophonology / Morphosyntax / Syntax: Introduction / Parts of speech / Heads, arguments, and adjuncts / Argument types and grammatical functions / Mismatches between syntactic position and semantic roles / Resources / Bibliography / Author's Biography / General Index / Index of Languages
Formal Analysis for Natural Language Processing: A Handbook by Zhiwei Feng Pdf
The field of natural language processing (NLP) is one of the most important and useful application areas of artificial intelligence. 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.
The Handbook of Computational Linguistics and Natural Language Processing by Alexander Clark,Chris Fox,Shalom Lappin Pdf
This comprehensive reference work provides an overview of the concepts, methodologies, and applications in computational linguistics and natural language processing (NLP). Features contributions by the top researchers in the field, reflecting the work that is driving the discipline forward Includes an introduction to the major theoretical issues in these fields, as well as the central engineering applications that the work has produced Presents the major developments in an accessible way, explaining the close connection between scientific understanding of the computational properties of natural language and the creation of effective language technologies Serves as an invaluable state-of-the-art reference source for computational linguists and software engineers developing NLP applications in industrial research and development labs of software companies
Natural Language Processing and Computational Linguistics by Bhargav Srinivasa-Desikan Pdf
Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and computational linguistics algorithms. Key Features Discover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and Keras Hands-on text analysis with Python, featuring natural language processing and computational linguistics algorithms Learn deep learning techniques for text analysis Book Description Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data. This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis. What you will learn Why text analysis is important in our modern age Understand NLP terminology and get to know the Python tools and datasets Learn how to pre-process and clean textual data Convert textual data into vector space representations Using spaCy to process text Train your own NLP models for computational linguistics Use statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learn Employ deep learning techniques for text analysis using Keras Who this book is for This book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!
Practical Natural Language Processing by Sowmya Vajjala,Bodhisattwa Majumder,Anuj Gupta,Harshit Surana Pdf
Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective
The People’s Web Meets NLP by Iryna Gurevych,Jungi Kim Pdf
Collaboratively Constructed Language Resources (CCLRs) such as Wikipedia, Wiktionary, Linked Open Data, and various resources developed using crowdsourcing techniques such as Games with a Purpose and Mechanical Turk have substantially contributed to the research in natural language processing (NLP). Various NLP tasks utilize such resources to substitute for or supplement conventional lexical semantic resources and linguistically annotated corpora. These resources also provide an extensive body of texts from which valuable knowledge is mined. There are an increasing number of community efforts to link and maintain multiple linguistic resources. This book aims offers comprehensive coverage of CCLR-related topics, including their construction, utilization in NLP tasks, and interlinkage and management. Various Bachelor/Master/Ph.D. programs in natural language processing, computational linguistics, and knowledge discovery can use this book both as the main text and as a supplementary reading. The book also provides a valuable reference guide for researchers and professionals for the above topics.
Explainable Natural Language Processing by Anders Søgaard Pdf
This book presents a taxonomy framework and survey of methods relevant to explaining the decisions and analyzing the inner workings of Natural Language Processing (NLP) models. The book is intended to provide a snapshot of Explainable NLP, though the field continues to rapidly grow. The book is intended to be both readable by first-year M.Sc. students and interesting to an expert audience. The book opens by motivating a focus on providing a consistent taxonomy, pointing out inconsistencies and redundancies in previous taxonomies. It goes on to present (i) a taxonomy or framework for thinking about how approaches to explainable NLP relate to one another; (ii) brief surveys of each of the classes in the taxonomy, with a focus on methods that are relevant for NLP; and (iii) a discussion of the inherent limitations of some classes of methods, as well as how to best evaluate them. Finally, the book closes by providing a list of resources for further research on explainability.
Handbook of Natural Language Processing by Robert Dale,Hermann Moisl,Harold Somers Pdf
This study explores the design and application of natural language text-based processing systems, based on generative linguistics, empirical copus analysis, and artificial neural networks. It emphasizes the practical tools to accommodate the selected system.
