Natural Language Processing: Python and NLTK

Nitin Hardeniya et al.



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.

Table of Contents

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
11: TOKENIZING TEXT AND WORDNET BASICS
12: REPLACING AND CORRECTING WORDS
13: CREATING CUSTOM CORPORA
14: PART-OF-SPEECH TAGGING
15: EXTRACTING CHUNKS
16: TRANSFORMING CHUNKS AND TREES
17: TEXT CLASSIFICATION
18: DISTRIBUTED PROCESSING AND HANDLING LARGE DATASETS
19: PARSING SPECIFIC DATA TYPES
20: WORKING WITH STRINGS
21: STATISTICAL LANGUAGE MODELING
22: MORPHOLOGY – GETTING OUR FEET WET
23: PARTS-OF-SPEECH TAGGING – IDENTIFYING WORDS
24: PARSING – ANALYZING TRAINING DATA
25: SEMANTIC ANALYSIS – MEANING MATTERS
26: SENTIMENT ANALYSIS – I AM HAPPY
27: INFORMATION RETRIEVAL – ACCESSING INFORMATION
28: DISCOURSE ANALYSIS – KNOWING IS BELIEVING
29: EVALUATION OF NLP SYSTEMS – ANALYZING PERFORMANCE

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