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Language Processing and Python It is easy to get our hands on millions of words of text. What can we do with it, assuming we words to Disambiguate Alphabet Letters from Others write some simple programs? What can we achieve by combining simple programming techniques with large quantities of text?
How can we automatically extract key words and phrases that sum up the style and content of a text? What tools and techniques does the Python programming language provide for such work? What are some of the interesting challenges of natural language processing? This chapter is divided into sections that skip between two quite different styles.
What is it about?
In the “computing with language” sections we will take on some linguistically motivated programming tasks without necessarily explaining how they work. In the “closer look at Python” sections we will systematically review key programming concepts. We’ll flag the two styles in the section titles, but later chapters will mix both styles without being so up-front about it. If the material is completely new to you, this chapter will raise more questions than it answers, questions that are addressed in the rest of this book. 1 Computing with Language: Texts and Words We’re all very familiar with text, since we read and write it every day. But before we can do this, we have to get started with the Python interpreter. Type “help”, “copyright”, “credits” or “license” for more information.
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If you are unable to run the Python interpreter, you probably don’t have Python installed correctly. Python interpreter is now waiting for input. Once the interpreter has finished calculating the answer and displaying it, the prompt reappears. This means the Python interpreter is waiting for another instruction.
Your Turn: Enter a few more expressions of your own. The preceding examples demonstrate how you can work interactively with the Python interpreter, experimenting with various expressions in the language to see what they do. In Python, it doesn’t make sense to end an instruction with a plus sign. Now that we can use the Python interpreter, we’re ready to start working with language data. 2 Getting Started with NLTK Before going further you should install NLTK 3.
Follow the instructions there to download the version required for your platform. Downloading the NLTK Book Collection: browse the available packages using nltk. The Collections tab on the downloader shows how the packages are grouped into sets, and you should select the line labeled book to obtain all data required for the examples and exercises in this book. It consists of about 30 compressed files requiring about 100Mb disk space. Once the data is downloaded to your machine, you can load some of it using the Python interpreter. Here’s the command again, together with the output that you will see.
Type the name of the text or sentence to view it. 9: The Man Who Was Thursday by G . Now that we can use the Python interpreter, and have some data to work with, we’re ready to get started. 3 Searching Text There are many ways to examine the context of a text apart from simply reading it.
A concordance view shows us every occurrence of a given word, together with some context. CHAPTER 55 Of the monstrous Pictures of Whales . The first time you use a concordance on a particular text, it takes a few extra seconds to build an index so that subsequent searches are fast. Ctrl-up-arrow or Alt-p to access the previous command and modify the word being searched. You can also try searches on some of the other texts we have included. Search the book of Genesis to find out how long some people lived, using text3. Note that this corpus is uncensored!
Once you’ve spent a little while examining these texts, we hope you have a new sense of the richness and diversity of language. In the next chapter you will learn how to access a broader range of text, including text in languages other than English. A concordance permits us to see words in context. What other words appear in a similar range of contexts? Observe that we get different results for different texts. It is one thing to automatically detect that a particular word occurs in a text, and to display some words that appear in the same context.
However, we can also determine the location of a word in the text: how many words from the beginning it appears. Each stripe represents an instance of a word, and each row represents the entire text. You can produce this plot as shown below. Can you predict the dispersion of a word before you view it?
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As before, take care to get the quotes, commas, brackets and parentheses exactly right. Lexical Dispersion Plot for Words in U. Presidential Inaugural Addresses: This can be used to investigate changes in language use over time. Now, just for fun, let’s try generating some random text in the various styles we have just seen.
We need to include the parentheses, but there’s nothing that goes between them. 0 but will be reinstated in a subsequent version. 4 Counting Vocabulary The most obvious fact about texts that emerges from the preceding examples is that they differ in the vocabulary they use. In this section we will see how to use the computer to count the words in a text in a variety of useful ways. As before, you will jump right in and experiment with the Python interpreter, even though you may not have studied Python systematically yet.
Let’s begin by finding out the length of a text from start to finish, in terms of the words and punctuation symbols that appear. So Genesis has 44,764 words and punctuation symbols, or “tokens. But there are only four distinct vocabulary items in this phrase. How many distinct words does the book of Genesis contain? To work this out in Python, we have to pose the question slightly differently. The vocabulary of a text is just the set of tokens that it uses, since in a set, all duplicates are collapsed together.
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When you do this, many screens of words will fly past. All capitalized words precede lowercase words. Although it has 44,764 tokens, this book has only 2,789 distinct words, or “word types. Now, let’s calculate a measure of the lexical richness of the text. Next, let’s focus on particular words. How much is this as a percentage of the total number of words in this text? You may want to repeat such calculations on several texts, but it is tedious to keep retyping the formula.
Instead, you can come up with your own name for a task, like “lexical_diversity” or “percentage”, and associate it with a block of code. Now you only have to type a short name instead of one or more complete lines of Python code, and you can re-use it as often as you like. It is up to you to do the indentation, by typing four spaces or hitting the tab key. To finish the indented block just enter a blank line. Functions are an important concept in programming, and we only mention them at the outset to give newcomers a sense of the power and creativity of programming. Don’t worry if you find it a bit confusing right now.
Later we’ll see how to use functions when tabulating data, as in 1. Each row of the table will involve the same computation but with different data, and we’ll do this repetitive work using a function. 2 A Closer Look at Python: Texts as Lists of Words You’ve seen some important elements of the Python programming language. Let’s take a few moments to review them systematically. At one level, it is a sequence of symbols on a page such as this one.
At another level, it is a sequence of chapters, made up of a sequence of sections, where each section is a sequence of paragraphs, and so on. However, for our purposes, we will think of a text as nothing more than a sequence of words and punctuation. Python: it is how we store a text. Repeat some of the other Python operations we saw earlier in 1, e.
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A pleasant surprise is that we can use Python’s addition operator on lists. We can concatenate sentences to build up a text. What if we want to add a single item to a list? 2 Indexing Lists As we have seen, a text in Python is a list of words, represented using a combination of brackets and quotes. With some patience, we can pick out the 1st, 173rd, or even 14,278th word in a printed text. Analogously, we can identify the elements of a Python list by their order of occurrence in the list. Indexes are a common way to access the words of a text, or, more generally, the elements of any list.
Thus, zero steps forward leaves it at the first element. This practice of counting from zero is initially confusing, but typical of modern programming languages. File “”, line 1, in ? This time it is not a syntax error, because the program fragment is syntactically correct. Let’s take a closer look at slicing, using our artificial sentence again.
We can modify an element of a list by assigning to one of its index values. Check your understanding by trying the exercises on lists at the end of this chapter. It saved a lot of typing to be able to refer to a 250,000-word book with a short name like this! In general, we can make up names for anything we care to calculate. We did this ourselves in the previous sections, e.
Python will evaluate the expression, and save its result to the variable. The equals sign is slightly misleading, since information is moving from the right side to the left. It might help to think of it as a left-arrow. The name of the variable can be anything you like, e. It must start with a letter, and can include numbers and underscores.
Remember that capitalized words appear before lowercase words in sorted lists. Python expressions can be split across multiple lines, so long as this happens within any kind of brackets. It doesn’t matter how much indentation is used in these continuation lines, but some indentation usually makes them easier to read. It is good to choose meaningful variable names to remind you — and to help anyone else who reads your Python code — what your code is meant to do.
We will often use variables to hold intermediate steps of a computation, especially when this makes the code easier to follow. Variable names cannot contain whitespace, but you can separate words using an underscore, e. We will come back to the topic of strings in 3. For the time being, we have two important building blocks — lists and strings — and are ready to get back to some language analysis. 3 Computing with Language: Simple Statistics Let’s return to our exploration of the ways we can bring our computational resources to bear on large quantities of text. We began this discussion in 1, and saw how to search for words in context, how to compile the vocabulary of a text, how to generate random text in the same style, and so on. In this section we pick up the question of what makes a text distinct, and use automatic methods to find characteristic words and expressions of a text.
As in 1, you can try new features of the Python language by copying them into the interpreter, and you’ll learn about these features systematically in the following section. Before continuing further, you might like to check your understanding of the last section by predicting the output of the following code. You can use the interpreter to check whether you got it right. If you’re not sure how to do this task, it would be a good idea to review the previous section before continuing further. 1 Frequency Distributions How can we automatically identify the words of a text that are most informative about the topic and genre of the text? Imagine how you might go about finding the 50 most frequent words of a book. One method would be to keep a tally for each vocabulary item, like that shown in 3.
In general, it could count any kind of observable event. It is a “distribution” because it tells us how the total number of word tokens in the text are distributed across the vocabulary items. Since we often need frequency distributions in language processing, NLTK provides built-in support for them. 260,819 in the case of Moby Dick.
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Be careful to use the correct parentheses and uppercase letters. Do any words produced in the last example help us grasp the topic or genre of this text? What proportion of the text is taken up with such words? We can generate a cumulative frequency plot for these words, using fdist1.