Natural Language Processing With Java Pdf Download

Natural Language Processing with Java
by Richard M Reese

If you are a Java programmer who wants to learn about the fundamental tasks underlying natural language processing, this book is for you. You will be able to identify and use NLP tasks for many common problems, and integrate them in your applications to solve more difficult problems. Readers should be familiar/experienced with Java software development.

Natural Language Processing with Java and LingPipe Cookbook
by Breck Baldwin, Krishna Dayanidhi

This book is for experienced Java developers with NLP needs, whether academics, industrialists, or hobbyists. A basic knowledge of NLP terminology will be beneficial.

Text Processing in Java
by Mitzi Morris

This book teaches you how to master the subtle art of multilingual text processing and prevent text data corruption. It provides an introduction to natural language processing using Lucene and Solr. It gives you tools and techniques to manage large collections of
text data, whether they come from news feeds, databases, or legacy documents. Each chapter contains executable programs that can also be used for text data forensics. Topics covered: -Unicode code points -Character encodings from ASCII and Big5 to UTF-8 and UTF-32LE -Character normalization using International Components for Unicode (ICU) -Java I/O, including working directly with zip, gzip, and tar files -Regular expressions in Java -Transporting text data via HTTP -Parsing and generating XML, HTML, and JSON -Using Lucene 4 for natural language search and text classification -Search, spelling correction, and clustering with Solr 4 Other books on text processing presuppose much of the material covered in this book.
They gloss over the details of transforming text from one format to another and assume perfect input data. The messy reality of raw text will have you reaching for this book again and again.

Deep Learning for Natural Language Processing
by Palash Goyal, Sumit Pandey, Karan Jain

Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models.

You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system.
This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways.
What You Will Learn

  • Gain the fundamentals of deep learning and its mathematical prerequisites
  • Discover deep learning frameworks in Python
  • Develop a chatbot
  • Implement a research paper on sentiment classification

Who This Book Is For
Software developers who are curious to try out deep learning with NLP.

Natural Language Processing with Python
by Steven Bird, Ewan Klein, Edward Loper

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.

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