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

NER-tagging examples and visualization

One of spaCy's most impressive offerings is its visualization suites and API, and in particular displaCy [17]. We discussed this in the previous chapter when visualizing part of speech tags. While it is most impressive in visualizing dependency parsing (which we will see next chapter), it doesn't do a half bad job with entities either.

Fig 6.4 An example from a news excerpt from an Elon Musk article on https://www.wired.com

We can see in the above example that spaCy has caught the entities quite well. Indeed, even the Elon Musk page is marked as an organization, which could be considered an organization. It could be the context of Tesla before it or official pages after it we cannot be sure. We do have an interesting mistake caught again here, where Twitter is a geopolitical entity. Again, we could let this slide if we...