Author: Nitin Hardeniya
Pub Date: 2016
ISBN: 978-1-78728-510-1
Pages: 687
Language: English
Format: EPUB/MOBI/AZW3/PDF (conv)
Size: 18 Mb
Learn to build expert NLP and machine learning projects using NLTK and other Python libraries
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
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
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.
+ Table of Contents
Part 1. Module 1
1. Introduction to Natural Language Processing
2. Text Wrangling and Cleansing
3. Part of Speech Tagging
4. Parsing Structure in Text
5. NLP Applications
6. Text Classification
7. Web Crawling
8. Using NLTK with Other Python Libraries
9. Social Media Mining in Python
10. Text Mining at Scale
Part 2. Module 2
1. Tokenizing Text and WordNet Basics
2. Replacing and Correcting Words
3. Creating Custom Corpora
4. Part-of-speech Tagging
5. Extracting Chunks
6. Transforming Chunks and Trees
7. Text Classification
8. Distributed Processing and Handling Large Datasets
9. Parsing Specific Data TypesPart 3. Module 3
1. Working with Strings
2. Statistical Language Modeling
3. Morphology – Getting Our Feet Wet
4. Parts-of-Speech Tagging – Identifying Words
5. Parsing – Analyzing Training Data
6. Semantic Analysis – Meaning Matters
7. Sentiment Analysis – I Am Happy
8. Information Retrieval – Accessing Information
9. Discourse Analysis – Knowing Is Believing
10. Evaluation of NLP Systems – Analyzing Performance