by Krishna Bhavsar, Naresh Kumar, Pratap Dangeti | Released April 2018 | ISBN: 9781789343335

Learn a practical viewpoint to understand and implement NLP solutions involving POS tagging, parsing, and much moreAbout This VideoLearn how to implement NLTK for various scenarios with the help of example-rich solutions to take you beyond basic Natural Language ProcessingRich collection of independent solutions that will come in handy when you are working with Natural Language Processing with PythonUse dictionaries to create your own named entities using this easy-to-follow guideIn DetailHave you ever faced challenges in understanding language and planning sentences while performing Natural Language Processing? Do you wish to overcome these problems and go beyond the basic techniques like bag-of-words?Well, now you can. This course is designed with advanced solutions that will take you from newbie to pro in performing Natural Language Processing with NLTK. In this course, you will come across various concepts covering natural language understanding, Natural Language Processing, and syntactic analysis.It consists of everything you need to efficiently use NLTK to implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master syntactic and semantic analysis.By the end of this course, you will have all the knowledge you need to implement Natural Language Processing with Python.All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Developing-NLP-Applications-Using-NLTK-in-Python Show and hide more

1. Chapter 1 : POS Tagging and Grammars
   • The Course Overview 00:03:26
   • Exploring the In-Built Tagger 00:02:08
   • Writing Your Own Tagger 00:05:43
   • Training Your Own Tagger 00:03:05
   • Learning to Write Your Own Grammar 00:01:55
   • Writing a Probabilistic CFG 00:02:29
   • Writing a Recursive CFG 00:02:10
2. Chapter 2 : Chunking, Sentence Parse, and Dependencies
   • Using the Built-In Chunker 00:02:03
   • Writing Your Own Simple Chunker 00:02:13
   • Training a Chunker 00:02:24
   • Parsing Recursive Descent 00:01:40
   • Parsing Shift-Reduce 00:01:42
   • Parsing Dependency Grammar and Projective Dependency 00:01:38
   • Parsing a Chart 00:02:57
3. Chapter 3 : Information Extraction and Text Classification
   • Using Inbuilt NERs 00:02:09
   • Creating, Inversing, and Using Dictionaries 00:03:45
   • Choosing the Feature Set 00:03:49
   • Segmenting Sentences Using Classification 00:02:32
   • Writing a POS Tagger with Context 00:02:29
4. Chapter 4 : Advanced NLP Concepts
   • Creating an NLP Pipeline 00:07:04
   • Solving the Text Similarity Problem 00:04:02
   • Resolving Anaphora 00:03:35
   • Disambiguating Word Sense 00:02:44
   • Performing Sentiment Analysis 00:03:01
   • Exploring Advanced Sentiment Analysis 00:03:04
   • Creating a Conversational Assistant or Chatbot 00:03:57
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