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Subscribe to our Newsletter Get the latest tips, news, and developments. 3 Processing Raw Text The most important source of texts is undoubtedly the Web. It’s convenient to have existing text collections to explore, such as the corpora we saw in the previous chapters. However, you probably have your own text sources in mind, and need to learn how to access them.
How can we write programs to access text from local files and from the web, in order to get hold of an unlimited range of language material? How can we split documents up into individual words and punctuation symbols, so we can carry out the same kinds of analysis we did with text corpora in earlier chapters? How can we write programs to produce formatted output and save it in a file? In order to address these questions, we will be covering key concepts in NLP, including tokenization and stemming. Along the way you will consolidate your Python knowledge and learn about strings, files, and regular expressions. Since so much text on the web is in HTML format, we will also see how to dispense with markup.
However, you may be interested in analyzing other texts from Project Gutenberg. URL to an ASCII text file. Text number 2554 is an English translation of Crime and Punishment, and we can access it as follows. This is the raw content of the book, including many details we are not interested in such as whitespace, line breaks and blank lines. For our language processing, we want to break up the string into words and punctuation, as we saw in 1.
Notice that NLTK was needed for tokenization, but not for any of the earlier tasks of opening a URL and reading it into a string. This is because each text downloaded from Project Gutenberg contains a header with the name of the text, the author, the names of people who scanned and corrected the text, a license, and so on. Sometimes this information appears in a footer at the end of the file. This was our first brush with the reality of the web: texts found on the web may contain unwanted material, and there may not be an automatic way to remove it. But with a small amount of extra work we can extract the material we need. Dealing with HTML Much of the text on the web is in the form of HTML documents.