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  3. Introduction to Natural Language Processing in Python
  • 1

    Regular expressions & word tokenization

    Free

    This chapter will introduce some basic NLP concepts, such as word tokenization and regular expressions to help parse text. You'll also learn how to handle non-English text and more difficult tokenization you might find.

  • 2

    Simple topic identification

    This chapter will introduce you to topic identification, which you can apply to any text you encounter in the wild. Using basic NLP models, you will identify topics from texts based on term frequencies. You'll experiment and compare two simple methods: bag-of-words and Tf-idf using NLTK, and a new library Gensim.

  • 3

    Named-entity recognition

    This chapter will introduce a slightly more advanced topic: named-entity recognition. You'll learn how to identify the who, what, and where of your texts using pre-trained models on English and non-English text. You'll also learn how to use some new libraries, polyglot and spaCy, to add to your NLP toolbox.

  • 4

    Building a "fake news" classifier

    You'll apply the basics of what you've learned along with some supervised machine learning to build a "fake news" detector. You'll begin by learning the basics of supervised machine learning, and then move forward by choosing a few important features and testing ideas to identify and classify fake news articles.

Initializing

Exercise

Spanish NER with polyglot

You'll continue your exploration of polyglot now with some Spanish annotation. This article is not written by a newspaper, so it is your first example of a more blog-like text. How do you think that might compare when finding entities?

The Text object has been created as txt, and each entity has been printed, as you can see in the IPython Shell.

Your specific task is to determine how many of the entities contain the words "Márquez" or "Gabo" - these refer to the same person in different ways!

Instructions

100 XP
  • Iterate over all of the entities of txt, using ent as your iterator variable.
  • Check whether the entity contains "Márquez" or "Gabo". If it does, increment count. Don't forget to include the accented á in "Márquez"!
  • Hit 'Submit Answer' to see what percentage of entities refer to Gabriel García Márquez (aka Gabo).