This poster draws upon my graduate studies in applied linguistics and my experience working with Python and the [Natural Language Toolkit (NLTK)](http://nltk.org), a Python library that provides a number of tools for working with language. The poster will demonstrate how the NLTK can be used to extract meaningful chunks of language from a text, specifically _noun phrases_, using regular expressions to carry out part of speech (POS) tagging. Relevant concepts in linguistics are introduced and illustrated using the NLTK. A fundamental idea for the presentation is _grammar as science_; audience members are inductively introduced to the idea of descriptive grammar and how to use language samples to extrapolate grammar rules & patterns. These skills are incredibly useful for anyone who works with text. The poster contents will be made available online via an iPython notebook. A draft version of [the notebook](http://nbviewer.ipython.org/github/lukewrites/NP_chunking_with_nltk/blob/master/NP_chunking_with_the_NLTK.ipynb) and [its source](https://github.com/lukewrites/NP_chunking_with_nltk) are online now.