Hardeniya Nitin et al. Natural Language Processing: Python and NLTK

Packt Publishing, 2016. — 687 p. — ISBN: 978-1-78728-510-1Book DescriptionNatural 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.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 Python1: 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