Research into Natural Language Processing - the use of computers to process language - has developed over the last couple of decades into one of the most vigorous and interesting areas of current work on language and communication. This book introduces the subject through the discussion and development of various computer programs which illustrate some of the basic concepts and techniques in the field. The programming language used is Prolog, which is especially well-suited for Natural Language Processing and those with little or no background in computing. Following the general introduction, the first section of the book presents Prolog, and the following chapters illustrate how various Natural Language Processing programs may be written using this programming language. Since it is assumed that the reader has no previous experience in programming, great care is taken to provide a simple yet comprehensive introduction to Prolog. Due to the 'user friendly' nature of Prolog, simple yet effective programs may be written from an early stage. The reader is gradually introduced to various techniques for syntactic processing, ranging from Finite State Network recognisors to Chart parsers. An integral element of the book is the comprehensive set of exercises included in each chapter as a means of cementing the reader's understanding of each topic. Suggested answers are also provided. An Introduction to Natural Language Processing Through Prolog is an excellent introduction to the subject for students of linguistics and computer science, and will be especially useful for those with no background in the subject.

This book teaches the principles of natural language processing and covers linguistics issues. It also details the language-processing functions involved, including part-of-speech tagging using rules and stochastic techniques. A key feature of the book is the author's hands-on approach throughout, with extensive exercises, sample code in Prolog and Perl, and a detailed introduction to Prolog. The book is suitable for researchers and students of natural language processing and computational linguistics.

This book's main goal is to show readers how to use the linguistic theory of Noam Chomsky, called Universal Grammar, to represent English, French, and German on a computer using the Prolog computer language. In so doing, it presents a follow-the-dots approach to natural language processing, linguistic theory, artificial intelligence, and expert systems. The basic idea is to introduce meaningful answers to significant problems involved in representing human language data on a computer.

An examination of natural language processing in Prolog for those who know Prolog but not linguistics, this book enables students to move quickly into writing and working in useful software. It features many working computer programs that implement subsystems of a natural language processor. These programs are designed to be understood in isolation from one another and are compatible with an Edinburgh-compatible Prolog implementation, such as Quintus, ESL, Arity and ALS.

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow Key Features Weave neural networks into linguistic applications across various platforms Perform NLP tasks and train its models using NLTK and TensorFlow Boost your NLP models with strong deep learning architectures such as CNNs and RNNs Book Description Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges. To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts. What you will learn Implement semantic embedding of words to classify and find entities Convert words to vectors by training in order to perform arithmetic operations Train a deep learning model to detect classification of tweets and news Implement a question-answer model with search and RNN models Train models for various text classification datasets using CNN Implement WaveNet a deep generative model for producing a natural-sounding voice Convert voice-to-text and text-to-voice Train a model to convert speech-to-text using DeepSpeech Who this book is for Hands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book.