Book Image

Natural Language Processing and Computational Linguistics

By : Bhargav Srinivasa-Desikan
Book Image

Natural Language Processing and Computational Linguistics

By: Bhargav Srinivasa-Desikan

Overview of this book

Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data. This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.
Table of Contents (22 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Topic coherence and evaluating topic models

In the previous sections, we spoke extensively about how topic models, in general, are rather qualitative in nature - it's difficult to put a number on how useful a topic model is. Despite this, there is a need to evaluate topic models, and the most popular method out there is topic coherence - and lucky for us, Gensim has quite an extensive suite of topic coherence methods for us to try out.

What exactly is topic coherence? Briefly put, it is a measure of how interpretable topics are for human beings. There are multiple coherence measures in topic modeling literature, and we won't be going through the theory for these, but the following links should walk you through the theory and intuition, if interested:

  1. What is topic coherence? [9]
  2. Exploring the Space of Topic Coherence Measures [10]

The first link is a Gensim blog post...