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 scikit-learn

Gensim isn't the only package offering us the ability to topic model: scikit-learn, while not dedicated for text, still offers fast implementations of LDA and Non-negative Matrix Factorization (NMF), which can help us identify topics.

We already discussed how LDA works, and the only difference between the Gensim and scikit-learn implementations are as follows:

  1. The perplexity bounds are not expected to agree exactly here because the bound is calculated differently in Gensim versus sklearn. These bounds are ways we calculate how topics converge in topic modeling algorithms.
  2. Sklearn uses cython which creates numerical 6th decimal point differences.

Non-negative matrix factorization (NMF) [15], unlike LDA, is not a method mostly limited to text mining (though interestingly, LDA's variants also have been used in genetics and image processing...