Nltk Examples
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Applied Text Analysis with Python
Author: Benjamin Bengfort
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2018-06-11
From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity
Natural Language Processing: Python and NLTK
Author: Nitin Hardeniya
language: en
Publisher: Packt Publishing Ltd
Release Date: 2016-11-22
Learn to build expert NLP and machine learning projects using NLTK and other Python libraries About This Book Break text down into its component parts for spelling correction, feature extraction, and phrase transformation Work through NLP concepts with simple and easy-to-follow programming recipes Gain insights into the current and budding research topics of NLP Who This Book Is For If you are an NLP or machine learning enthusiast and an intermediate Python programmer who wants to quickly master NLTK for natural language processing, then this Learning Path will do you a lot of good. Students of linguistics and semantic/sentiment analysis professionals will find it invaluable. What You Will Learn The scope of natural language complexity and how they are processed by machines Clean and wrangle text using tokenization and chunking to help you process data better Tokenize text into sentences and sentences into words Classify text and perform sentiment analysis Implement string matching algorithms and normalization techniques Understand and implement the concepts of information retrieval and text summarization Find out how to implement various NLP tasks in Python In Detail Natural Language Processing is a field of computational linguistics and artificial intelligence that deals with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning. The number of human-computer interaction instances are increasing so it's becoming imperative that computers comprehend all major natural languages. The first NLTK Essentials module is an introduction on how to build systems around NLP, with a focus on how to create a customized tokenizer and parser from scratch. You will learn essential concepts of NLP, be given practical insight into open source tool and libraries available in Python, shown how to analyze social media sites, and be given tools to deal with large scale text. This module also provides a workaround using some of the amazing capabilities of Python libraries such as NLTK, scikit-learn, pandas, and NumPy. The second Python 3 Text Processing with NLTK 3 Cookbook module teaches you the essential techniques of text and language processing with simple, straightforward examples. This includes organizing text corpora, creating your own custom corpus, text classification with a focus on sentiment analysis, and distributed text processing methods. The third Mastering Natural Language Processing with Python module will help you become an expert and assist you in creating your own NLP projects using NLTK. You will be guided through model development with machine learning tools, shown how to create training data, and given insight into the best practices for designing and building NLP-based applications using Python. This Learning Path combines some of the best that Packt has to offer in one complete, curated package and is designed to help you quickly learn text processing with Python and NLTK. It includes content from the following Packt products: NTLK essentials by Nitin Hardeniya Python 3 Text Processing with NLTK 3 Cookbook by Jacob Perkins Mastering Natural Language Processing with Python by Deepti Chopra, Nisheeth Joshi, and Iti Mathur Style and approach This comprehensive course creates a smooth learning path that teaches you how to get started with Natural Language Processing using Python and NLTK. You'll learn to create effective NLP and machine learning projects using Python and NLTK.
Natural Language Processing with Python
Author: Steven Bird
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2009-06-12
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.