Handbook Of Computational Linguistics And Natural Language Processing

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

The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.New to the Second EditionGreater

Ruslan Mitkov's highly successful Oxford Handbook of Computational Linguistics has been substantially revised and expanded in this second edition. Alongside updated accounts of the topics covered in the first edition, it includes 17 new chapters on subjects such as semantic role-labelling, text-to-speech synthesis, translation technology, opinion mining and sentiment analysis, and the application of Natural Language Processing in educational and biomedical contexts, among many others. The volume is divided into four parts that examine, respectively: the linguistic fundamentals of computational linguistics; the methods and resources used, such as statistical modelling, machine learning, and corpus annotation; key language processing tasks including text segmentation, anaphora resolution, and speech recognition; and the major applications of Natural Language Processing, from machine translation to author profiling. The book will be an essential reference for researchers and students in computational linguistics and Natural Language Processing, as well as those working in related industries.

This handbook of computational linguistics, written for academics, graduate students and researchers, provides a state-of-the-art reference to one of the most active and productive fields in linguistics.

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!

Computational linguistics deals with the study of the morphology of language as well as its syntax and dynamic use in order to enable machines process human language. There are numerous applications of this field, such as text-to-speech software, speech recognition and grammar checking. The ability of a computer program to understand human language is known as natural language processing. The two main techniques used with natural language processing are semantic and syntax analysis. Some of the syntax techniques are parsing, word segmentation and sentence breaking. The techniques which are associated with semantics are word sense disambiguation, named entity recognition and natural language generation. This book provides significant information of this discipline to help develop a good understanding of computational linguistics and natural language processing. It will serve as a valuable source of reference for graduate and post graduate students.

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.