Book Image

Mastering Natural Language Processing with Python [Video]

By : Deepti Chopra, Iti Mathur, Nisheeth Joshi
Book Image

Mastering Natural Language Processing with Python [Video]

By: Deepti Chopra, Iti Mathur, Nisheeth Joshi

Overview of this book

<p>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 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.</p> <p>The code bundle for the video course is available at -&nbsp;<a href="https://github.com/PacktPublishing/Mastering-Natural-Language-Processing-with-Python-Video" target="_blank">https://github.com/PacktPublishing/Mastering-Natural-Language-Processing-with-Python-Video</a></p> <h1>Style and Approach</h1> <p>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.</p>
Table of Contents (5 chapters)
Chapter 2
Parts-of-Speech Tagging – Identifying Words
Content Locked
Section 3
Statistical Modeling Involving the n-gram Approach
You now know that unigram means a single word and in a unigram tagger, a single token is used to find the particular parts-of-speech tag. So, how could you train UnigramTagger by providing it with a list of sentences at the time of initialization? Let’s try to get an answer to this question. - Perform training using the first 7000 sentences of the Treebank corpus - Evaluate UnigramTagger by calculating the accuracy - Use back-off tagging