This chapter covers parsing specific kinds of data, focusing primarily on dates, times, and HTML. Luckily, there are a number of useful libraries for accomplishing this, so we don't have to delve into tricky and overly complicated regular expressions. These libraries can be great complements to the NLTK:

The libraries can be useful for pre-processing text before passing it to an NLTK object, or post-processing text that has been processed and extracted using NLTK. Here's an example that ties many of these tools together.

Let's say you need to parse a blog article about a restaurant. You can use lxml or BeautifulSoup to extract the article text, outbound links, and the date and time when the article was written. The date and time can then be parsed to a Python datetime object with dateutil. Once you have the article text, you can use chardet to ensure it's UTF-8 before cleaning out the HTML and running it through NLTK-based part-of-speech tagging, chunk extraction, and/or text classification, to create additional metadata about the article. If there's an event happening at the restaurant, you may be able to discover that by looking at the time words identified by timex. The point of this example is that real-world text processing often requires more than just NLTK-based natural language processing, and the functionality covered in this chapter can help with those additional requirements.