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 models in Gensim

Gensim [2] is arguably the most popular topic modeling toolkit freely available, and it being in Python means that it fits right into our ecosystem. Gensim's popularity is because of its wide variety of topic modeling algorithms, straightforward API, and active community. Of course, we have already introduced Gensim before, in Chapter 4, Gensim - Vectorizing Text and Transformations and n-grams, on vector spaces. We would be needing to know how to set up our corpus for the topic modeling algorithms we will be using, so now is a good time to brush on the contents of the Vector transformation in Gensim section, in Chapter 4, Gensim - Vectorizing Text and Transformations and n-grams.

All done? Now we can start using the powerful tools that Gensim have to offer. The Jupyter notebook [7] runs us through the same corpus generating techniques we previously...