Book Image

Developing NLP Applications Using NLTK in Python [Video]

By : Krishna Bhavsar, V Naresh Kumar, Pratap Dangeti
Book Image

Developing NLP Applications Using NLTK in Python [Video]

By: Krishna Bhavsar, V Naresh Kumar, Pratap Dangeti

Overview of this book

<p>Have 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?</p> <p>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.</p> <p>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.</p> <p>By the end of this course, you will have all the knowledge you need to implement Natural Language Processing with Python.</p> <p>All the code and supporting files for this course are available on Github at <a style="color: #fa8d11;" href="https://github.com/PacktPublishing/Developing-NLP-Applications-Using-NLTK-in-Python" target="blank">https://github.com/PacktPublishing/Developing-NLP-Applications-Using-NLTK-in-Python</a></p> <h1>Style and Approach</h1> <p>The standalone solutions of this course will teach you how to efficiently perform Natural Language Processing in Python. It covers state-of-the-art techniques necessary for applications in NLP. Addressing your common and not-so-common pain points, this is a course that you must have on your library.</p>
Table of Contents (4 chapters)
Chapter 3
Information Extraction and Text Classification
Content Locked
Section 3
Choosing the Feature Set
Choosing the feature set Features are one of the most powerful components of nltk library. They represent clues within the language for easy tagging of the data that we are dealing with. - Create learnSimpleFeatures() - Create learnFeatures() - Compare both the functions