Book Image

Mastering Natural Language Processing with Python

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

Mastering Natural Language Processing with Python

By: Deepti Chopra, Nisheeth Joshi, Iti Mathur

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.</p> <p>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.</p> <p>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, Sentiment Analysis, Text Summarization, and Anaphora Resolution.</p>
Table of Contents (17 chapters)
Mastering Natural Language Processing with Python
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Developing an IR system using latent semantic indexing


Latent semantic indexing can be used for performing categorization with the help of minimum training.

Latent semantic indexing is a technique that can be used for processing text. It can perform the following:

  • Automatic categorization of text

  • Conceptual information retrieval

  • Cross-lingual information retrieval

Latent semantic method may be defined as an information retrieval and indexing method. It makes use of a mathematical technique known as Singular Value Decomposition (SVD). SVD is used for the detection of patterns having a certain relation with the concepts contained in a given unstructured text.

Some of the applications of latent semantic indexing include the following:

  • Information discovery

  • Automated document classification text summarization[20] (eDiscovery, Publishing)

  • Relationship discovery

  • Automatic generation of the link charts of individuals and organizations

  • Matching technical papers and grants with reviewers

  • Online customer support...