Mastering Natural Language Processing with Python by Deepti Chopra, Nisheeth Joshi, Iti Mathur
2016 | ISBN: 1783989041 | English | 238 pages | PDF | 2 MB
Maximize your NLP capabilities while creating amazing NLP projects in Python
About This Book
Learn to implement various NLP tasks in Python
Gain insights into the current and budding research topics of NLP
This is a comprehensive step-by-step guide to help students and researchers create their own projects based on real-life applications
Who This Book Is For
This book is for intermediate level developers in NLP with a reasonable knowledge level and understanding of Python.
What You Will Learn
Implement string matching algorithms and normalization techniques
Implement statistical language modeling techniques
Get an insight into developing a stemmer, lemmatizer, morphological analyzer, and morphological generator
Develop a search engine and implement POS tagging concepts and statistical modeling concepts involving the n gram approach
Familiarize yourself with concepts such as the Treebank construct, CFG construction, the CYK Chart Parsing algorithm, and the Earley Chart Parsing algorithm
Develop an NER-based system and understand and apply the concepts of sentiment analysis
Understand and implement the concepts of Information Retrieval and text summarization
Develop a Discourse Analysis System and Anaphora Resolution based system
Natural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned 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.
This book will give you expertise on how to employ various NLP tasks in Python, giving you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and assist you in creating your own NLP projects using NLTK.
You will sequentially be guided through applying machine learning tools to develop various models. Well give you clarity on how to create training data and how to implement major NLP applications such as Named Entity Recognition, Question Answering System, Discourse Analysis, Transliteration, Word Sense disambiguation, Information Retrieval, Sentiment Analysis, Text Summarization, and Anaphora Resolution.
Style and approach
This is an easy-to-follow guide, full of hands-on examples of real-world tasks. Each topic is explained and placed in context, and for the more inquisitive, there are more details of the concepts used.