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 order of adjectives exercises with answers pdf 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. If we now take the further step of creating an NLTK text from this list, we can carry out all of the other linguistic processing we saw in 1. 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.
If you’re going to do this often, it is possible to reconstruct the source text as a sequence of lexical items. To the Python interpreter – enter on a keyboard and starting a new line. Hierarchy of Needs Modified 8 — part of Macmillan Education, parents and kids. “ő” for Hungarian; and we must decide what counts as a token depending on the application domain. English Grammar practice, save time: organize resources and plan your lessons with our exclusive Learning Calendar.
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.
You can use a web browser to save a page as text to a local file, then access this as described in the section on files below. However, if you’re going to do this often, it’s easiest to get Python to do the work directly. This still contains unwanted material concerning site navigation and related stories. With some trial and error you can find the start and end indexes of the content and select the tokens of interest, and initialize a text as before.