Natural Language Parsing with Regular Expressions
Lesson 1 of 1
  1. 1
    Discovering new code words in declassified CIA documents may seem like a mission for a foreign intelligence service, and detecting gender biases in the Harry Potter novels a task for a literature p…
  2. 2
    Before you dive into more complex syntax parsing, you’ll begin with basic regular expressions in Python using the re module as a regex refresher. The first method you will explore is _**.compi…
  3. 3
    You can make your regular expression matches even more dynamic with the help of the .search() method. Unlike .match() which will only find matches at the start of a string, .search() will loo…
  4. 4
    While it is useful to match and search for patterns of individual characters in a text, you can often find more meaning by analyzing text on a word-by-word basis, focusing on the part of speech of …
  5. 5
    You have made it to the juicy stuff! Given your part-of-speech tagged text, you can now use regular expressions to find patterns in sentence structure that give insight into the meaning of a text. …
  6. 6
    While you are able to chunk any sequence of parts of speech that you like, there are certain types of chunking that are linguistically helpful for determining meaning and bias in a piece of text. O…
  7. 7
    Another popular type of chunking is VP-chunking, or verb phrase chunking. A verb phrase is a phrase that contains a verb and its complements, objects, or modifiers. Verb phrases can take…
  8. 8
    Another option you have to find chunks in your text is chunk filtering. Chunk filtering lets you define what parts of speech you do not want in a chunk and remove them. A popular method …
  9. 9
    And there you go! Now you have the toolkit to dig into any piece of text data and perform natural language parsing with regular expressions. What insights will you gain, or what bias may you uncove…

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