Each of the 10 word lists contains 100 important words. Make a commitment to learn one list a week. Word smart ii pdf – work on vocabulary is never a waste of time. It pays dividends in terms of your final SAT score, but more importantly, it makes you a more educated person.
We have also produced 10 vocabulary lists for you to refer to when doing the real tests from the Official SAT study guide. Word Focus is our systematic approach to advanced vocabulary building. Find out more about Word Focus. More information For more information and tips on how to use these word lists, visit our word lists section. Technologies: a Friend or a Foe? Whole Foods Market, 2005: Will There Be Enough Organic Food to Satisfy the Growing Demand?
Do Childern Learn Better in Boys-Only and Girls-Only Schools? Case Analysis: Blanchard Importing and Distributing Co. How Can Resourcing and Development Add Value to the Modern Workplace? 0: Do Companies Need It to Survive? Only Autonomous Decisions Which Are Worthy of Protection Are Those Based Upon Sound Moral Values. Compare and Contract the Budget Processes and Systems of Fiscal Accountability in Presidential and Parliamentary Systems of Government.
SAT is a registered trademark of the College Board, which was not involved in the production of, and does not endorse, this product. The clouds give greater prominence to words that appear more frequently in the source text. You can tweak your clouds with different fonts, layouts, and color schemes. The images you create with Wordle are yours to use however you like. You can print them out, or save them to your own desktop to use as you wish. 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. 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. 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. Processing Search Engine Results The web can be thought of as a huge corpus of unannotated text. Web search engines provide an efficient means of searching this large quantity of text for relevant linguistic examples.
The main advantage of search engines is size: since you are searching such a large set of documents, you are more likely to find any linguistic pattern you are interested in. Unfortunately, search engines have some significant shortcomings. First, the allowable range of search patterns is severely restricted. Unlike local corpora, where you write programs to search for arbitrarily complex patterns, search engines generally only allow you to search for individual words or strings of words, sometimes with wildcards. Processing RSS Feeds The blogosphere is an important source of text, in both formal and informal registers. With some further work, we can write programs to create a small corpus of blog posts, and use this as the basis for our NLP work. Suppose you have a file document.
Your Turn: Create a file called document. If you are using IDLE, select the New Window command in the File menu, typing the required text into this window, and then saving the file as document. IDLE offers in the pop-up dialogue box. Various things might have gone wrong when you tried this.
IOError: No such file or directory: ‘document. Another possible problem you might have encountered when accessing a text file is the newline conventions, which are different for different operating systems. Universal”, which lets us ignore the different conventions used for marking newlines. Assuming that you can open the file, there are several methods for reading it. Enter on a keyboard and starting a new line.