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

Summarizing text

Often in text analysis, it is useful to summarize large bodies of text either to have a brief overlook of the text before deeply analyzing it or identifying the keywords in a text. It is also often the end game a text analysis task of its own. We will not be working on building our own text summarization pipeline, but rather focus on using the built-in summarization API which Gensim offers us.

It is important to remember that the algorithms included in Gensim do not create its own sentences, but rather extracts the key sentences from the text which we run the algorithm on. This summarizer is based on the TextRank algorithm, from an article by Mihalcea and others, called TextRank [10]. This algorithm was later improved upon by Barrios and others in another article, Variations of the Similarity Function of TextRank for Automated Summarization ...