by Deepti Chopra, Nisheeth Joshi, Iti Mathur | Released August 2018 | ISBN: 9781789618358

Explore various NLP tasks while enhancing your Python skills in real-world scenariosAbout This VideoLearn to implement various NLP tasks in PythonGain insights into the current and budding research topics of NLPThis is a comprehensive step-by-step guide to help students and researchers create their own projects based on real-life applicationsIn DetailNatural 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 course 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. We'll 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, Text Summarization, and Anaphora Resolution.The code bundle for the video course is available at -https://github.com/PacktPublishing/Mastering-Natural-Language-Processing-with-Python-Video Show and hide more

1. Chapter 1 : Working with Strings
   • The Course Overview 00:04:33
   • Tokenization 00:07:47
   • Normalization 00:04:25
   • Substituting and Correcting Tokens 00:08:30
   • Similarity Measures 00:03:52
   • Understanding Word Frequency 00:05:50
2. Chapter 2 : Parts-of-Speech Tagging – Identifying Words
   • Introducing Parts-of-Speech Tagging 00:03:35
   • Default Tagging 00:04:37
   • Statistical Modeling Involving the n-gram Approach 00:04:54
   • Developing a Chunker Using POS-tagged Corpora 00:02:02
3. Chapter 3 : Parsing – Analyzing Training Data
   • Treebank Construction 00:05:00
   • Extracting Context Free Grammar (CFG) Rules from Treebank 00:03:52
   • Creating a Probabilistic Context Free Grammar from CFG 00:03:13
4. Chapter 4 : Semantic Analysis – Meaning Matters
   • Introducing Semantic Analysis 00:03:25
   • Introducing NER 00:02:33
   • An NER System Using Hidden Markov Model 00:05:00
   • Generation of the synset id from Wordnet 00:04:42
5. Chapter 5 : Discourse Analysis and Evaluation of NLP Systems
   • Introducing Discourse Analysis 00:07:11
   • Anaphora Resolution 00:04:10
   • Evaluation of NLP Tools 00:08:08
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