1   Introduction to Natural Language Processing and Python

1.1.1   The Richness of Language

Language is the chief manifestation of human intelligence. Through language we express basic needs and lofty aspirations, technical know-how and flights of fantasy. Ideas are shared over great separations of distance and time. The following samples from English illustrate the richness of language:

Thanks to this richness, the study of language is part of many disciplines outside of linguistics, including translation, literary criticism, philosophy, anthropology and psychology. Many less obvious disciplines investigate language use, such as law, hermeneutics, forensics, telephony, pedagogy, archaeology, cryptanalysis and speech pathology. Each applies distinct methodologies to gather observations, develop theories and test hypotheses. Yet all serve to deepen our understanding of language and of the intellect that is manifested in language.

The importance of language to science and the arts is matched in significance by the cultural treasure embodied in language. Each of the world's ~7,000 human languages is rich in unique respects, in its oral histories and creation legends, down to its grammatical constructions and its very words and their nuances of meaning. Threatened remnant cultures have words to distinguish plant subspecies according to therapeutic uses that are unknown to science. Languages evolve over time as they come into contact with each other and they provide a unique window onto human pre-history. Technological change gives rise to new words like blog and new morphemes like e- and cyber-. In many parts of the world, small linguistic variations from one town to the next add up to a completely different language in the space of a half-hour drive. For its breathtaking complexity and diversity, human language is as a colorful tapestry stretching through time and space.

1.1.2   The Promise of NLP

As we have seen, NLP is important for scientific, economic, social, and cultural reasons. NLP is experiencing rapid growth as its theories and methods are deployed in a variety of new language technologies. For this reason it is important for a wide range of people to have a working knowledge of NLP. Within industry, it includes people in human-computer interaction, business information analysis, and Web software development. Within academia, this includes people in areas from humanities computing and corpus linguistics through to computer science and artificial intelligence. We hope that you, a member of this diverse audience reading these materials, will come to appreciate the workings of this rapidly growing field of NLP and will apply its techniques in the solution of real-world problems.

One of the friendly things about Python is that it allows you to type directly into the interactive interpreter — the program that will be running your Python programs. You can run the Python interpreter using a simple graphical interface called the Interactive DeveLopment Environment (IDLE). On a Mac you can find this under Applications -> MacPython, and on Windows under All Programs -> Python. Under Unix you can run Python from the shell by typing python. The interpreter will print a blurb about your Python version; simply check that you are running Python 2.4 or greater (here it is 2.5):

Python 2.5 (r25:51918, Sep 19 2006, 08:49:13)
[GCC 4.0.1 (Apple Computer, Inc. build 5341)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>

The >>> prompt indicates that the Python interpreter is now waiting for input. Let's begin by using the Python prompt as a calculator:

>>> 1 + 5 * 2 - 3
8
>>>

Once the interpreter has finished calculating the answer and displaying it, the prompt reappears. This means the Python interpreter is waiting for another instruction.

Try a few more expressions of your own. You can use asterisk (*) for multiplication and slash (/) for division, and parentheses for bracketing expressions. One strange thing you might come across is that division doesn't always behave how you expect:

>>> 3/3
1
>>> 1/3
0
>>>

The second case is surprising because we would expect the answer to be 0.333333. We will come back to why that is the case later on in this chapter. For now, let's simply observe that these examples demonstrate how you can work interactively with the interpreter, allowing you to experiment and explore. Also, as you will see later, your intuitions about numerical expressions will be useful for manipulating other kinds of data in Python.

You should also try nonsensical expressions to see how the interpreter handles it:

>>> 1 +
Traceback (most recent call last):
  File "<stdin>", line 1
    1 +
      ^
SyntaxError: invalid syntax
>>>

Here we have produced a syntax error. It doesn't make sense to end an instruction with a plus sign. The Python interpreter indicates the line where the problem occurred.

>>> from nltk.book import *
>>> text1
<Text: Moby Dick by Herman Melville 1851>

>>> text1.concordance('monstrous')
mong the former , one was of a most monstrous size . ... This came towards us , o
ION OF THE PSALMS . " Touching that monstrous bulk of the whale or ork we have re
all over with a heathenish array of monstrous clubs and spears . Some were thickl
ed as you gazed , and wondered what monstrous cannibal and savage could ever have
 that has survived the flood ; most monstrous and most mountainous ! That Himmale
 they might scout at Moby Dick as a monstrous fable , or still worse and more det
ath of Radney .'" CHAPTER 55 Of the monstrous Pictures of Whales . I shall ere lo
ling Scenes . In connexion with the monstrous pictures of whales , I am strongly

If we can find words in a text, we can also take note of their position within the text. We produce a dispersion plot, where each bar represents an instance of a word and each row represents the entire text. In Figure 1.1 we see characteristically different roles played by the male and female protagonists in Sense and Sensibility. In Figure 1.2 we see some striking patterns of word usage over the last 220 years. You can produce these plots as shown below. You might like to try different words, and different texts.

>>> text2.dispersion_plot(['Elinor', 'Marianne', 'Edward', 'Willoughby'])
>>> text4.dispersion_plot(['citizens', 'democracy', 'freedom', 'duties', 'America'])

Figure 1.1: Lexical Dispersion Plot for Main Protagonists in Sense and Sensibility

Figure 1.2: Lexical Dispersion Plot for Words in Presidential Inaugural Addresses

A concordance permits us to see words in context, e.g. we saw that monstrous appeared in the context the monstrous pictures. What other words appear in the same contexts that monstrous appears in? We can find out as follows:

>>> text1.similar('monstrous')
subtly impalpable curious abundant perilous trustworthy untoward
singular imperial few maddens loving mystifying christian exasperate
puzzled fearless uncommon domineering candid
>>> text2.similar('monstrous')
great very so good vast a exceedingly heartily amazingly as sweet
remarkably extremely

Observe that we get different results for different books.

Now, just for fun, let's try generating some random text in the various styles we have just seen. To do this, we type the name of the text followed by the "generate" function, e.g. text3.generate():

>>> text3.generate()
In the beginning of his brother is a hairy man , whose top may reach
unto heaven ; and ye shall sow the land of Egypt there was no bread in
all that he was taken out of the month , upon the earth . So shall thy
wages be ? And they made their father ; and Isaac was old , and kissed
him : and Laban with his cattle in the midst of the hands of Esau thy
first born , and Phichol the chief butler unto his son Isaac , she

Note that first time you run this, it is slow because it gathers statistics about word sequences. Each time you run it, you will get different output text. Now try generating random text in the style of an inaugural address or an internet chat room.

1.2.3   Counting Vocabulary

The most obvious fact about texts that emerges from the previous section 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. Let's look at the text of Moby Dick:

>>> len(text1)
260819

That's a quarter of a million words long! How many distinct words does this text 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 words that it uses, and in Python we can list the vocabulary of text3 with the command: set(text3). This will produce many screens of words. Now try the following:

>>> sorted(set(text3))
['!', "'", '(', ')', ',', ',)', '.', '.)', ':', ';', ';)', '?', '?)',
'A', 'Abel', 'Abelmizraim', 'Abidah', 'Abide', 'Abimael', 'Abimelech',
'Abr', 'Abrah', 'Abraham', 'Abram', 'Accad', 'Achbor', 'Adah', ...]
>>> len(set(text3))
2789
>>> len(text3) / len(set(text3))
16

Thus we can see a sorted list of vocabulary items beginning with various punctuation symbols. We can find out the size of the vocabulary by asking for the length of the set. Finally, we can calculate a measure of the lexical richness of the text and learn that each word is used 16 times on average.

We might like to repeat the last of these calculations on several texts, but it is tedious to keep retyping this line for different texts. Instead, we can come up with our own name for this task, e.g. "score", and define a function that can be re-used as often as we like:

>>> def score(text):
...     return len(text) / len(set(text))
...
>>> score(text3)
16
>>> score(text4)
4

Notice that we used the score function by typing its name, followed by an open parenthesis, the name of the text, then a close parenthesis. This is just what we did for the len and set functions earlier. These parentheses will show up often: their role is to separate the name of a task — such as score — from the data that the task is to be performed on — such as text3.

Now that we've had an initial sample of language processing in Python, we will continue with a systematic introduction to the language.

1.3.1   Representing text

We can't simply type text directly into the interpreter because it would try to interpret the text as part of the Python language:

>>> Hello World
Traceback (most recent call last):
  File "<stdin>", line 1
    Hello World
              ^
SyntaxError: invalid syntax
>>>

Here we see an error message. Note that the interpreter is confused about the position of the error, and points to the end of the string rather than the start.

Python represents a piece of text using a string. Strings are delimited — or separated from the rest of the program — by quotation marks:

>>> 'Hello World'
'Hello World'
>>> "Hello World"
'Hello World'
>>>

>>> 'Hello' + 'World'
'HelloWorld'
>>>

When applied to strings, the + operation is called concatenation. It produces a new string that is a copy of the two original strings pasted together end-to-end. Notice that concatenation doesn't do anything clever like insert a space between the words. The Python interpreter has no way of knowing that you want a space; it does exactly what it is told. Given the example of +, you might be able guess what multiplication will do:

>>> 'Hi' + 'Hi' + 'Hi'
'HiHiHi'
>>> 'Hi' * 3
'HiHiHi'
>>>

After a while, it can get quite tiresome to keep retyping Python statements over and over again. It would be nice to be able to store the value of an expression like 'Hi' + 'Hi' + 'Hi' so that we can use it again. We do this by saving results to a location in the computer's memory, and giving the location a name. Such a named place is called a variable. In Python we create variables by assignment, which involves putting a value into the variable:

>>> msg = 'Hello World'                           
>>> msg                                           
'Hello World'                                     # [_hw-output]
>>>

In line we have created a variable called msg (short for 'message') and set it to have the string value 'Hello World'. We used the = operation, which assigns the value of the expression on the right to the variable on the left. Notice the Python interpreter does not print any output; it only prints output when the statement returns a value, and an assignment statement returns no value. In line we inspect the contents of the variable by naming it on the command line: that is, we use the name msg. The interpreter prints out the contents of the variable in line .

Variables stand in for values, so instead of writing 'Hi' * 3 we could assign variable msg the value 'Hi', and num the value 3, then perform the multiplication using the variable names:

>>> msg = 'Hi'
>>> num = 3
>>> msg * num
'HiHiHi'
>>>

The names we choose for the variables are up to us. Instead of msg and num, we could have used any names we like:

>>> marta = 'Hi'
>>> foo123 = 3
>>> marta * foo123
'HiHiHi'
>>>

Thus, the reason for choosing meaningful variable names is to help you — and anyone who reads your code — to understand what it is meant to do. Python does not try to make sense of the names; it blindly follows your instructions, and does not object if you do something potentially confusing such as assigning a variable two the value 3, with the assignment statement: two = 3.

Note that we can also assign a new value to a variable just by using assignment again:

>>> msg = msg * num
>>> msg
'HiHiHi'
>>>

Here we have taken the value of msg, multiplied it by 3 and then stored that new string (HiHiHi) back into the variable msg.

1.3.3   Printing and Inspecting Strings

So far, when we have wanted to look at the contents of a variable or see the result of a calculation, we have just typed the variable name into the interpreter. We can also see the contents of msg using print msg:

>>> msg = 'Hello World'
>>> msg
'Hello World'
>>> print msg
Hello World
>>>

On close inspection, you will see that the quotation marks that indicate that Hello World is a string are missing in the second case. That is because inspecting a variable, by typing its name into the interactive interpreter, prints out the Python representation of a value. In contrast, the print statement only prints out the value itself, which in this case is just the text contained in the string.

>>> msg2 = 'Goodbye'
>>> print msg, msg2
Hello World Goodbye
>>>

You need to be a little bit careful in your choice of names (or identifiers) for Python variables. Some of the things you might try will cause an error. First, you should start the name with a letter, optionally followed by digits (0 to 9) or letters. Thus, abc23 is fine, but 23abc will cause a syntax error. You can use underscores (both within and at the start of the variable name), but not a hyphen, since this gets interpreted as an arithmetic operator. A second problem is shown in the following snippet.

>>> not = "don't do this"
  File "<stdin>", line 1
    not = "don't do this"
    ^
SyntaxError: invalid syntax

Why is there an error here? Because not is reserved as one of Python's 30 odd keywords. These are special identifiers that are used in specific syntactic contexts, and cannot be used as variables. It is easy to tell which words are keywords if you use IDLE, since they are helpfully highlighted in orange.

1. ☼ Start up the Python interpreter (e.g. by running IDLE). Try the examples in section 1.2, then experiment with using Python as a calculator.

2. ☼ Try the examples in this section, then try the following.

   1. Create a variable called msg and put a message of your own in this variable. Remember that strings need to be quoted, so you will need to type something like:

      >>> msg = "I like NLP!"

   2. Now print the contents of this variable in two ways, first by simply typing the variable name and pressing enter, then by using the print command.

   3. Try various arithmetic expressions using this string, e.g. msg + msg, and 5 * msg.

   4. Define a new string hello, and then try hello + msg. Change the hello string so that it ends with a space character, and then try hello + msg again.

3. ☺ Discuss the steps you would go through to find the ten most frequent words in a two-page document.

>>> msg = 'Hello World'
>>> msg[0]
'H'
>>> msg[3]
'l'
>>> msg[5]
' '
>>>

This is called indexing or subscripting the string. The position we specify inside the square brackets is called the index. We can retrieve not only letters but any character, such as the space at index 5.

The fact that strings are indexed from zero may seem counter-intuitive. You might just want to think of indexes as giving you the position in a string immediately before a character, as indicated in Figure 1.3.

Figure 1.3: String Indexing

Now, what happens when we try to access an index that is outside of the string?

>>> msg[11]
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
IndexError: string index out of range
>>>

The index of 11 is outside of the range of valid indices (i.e., 0 to 10) for the string 'Hello World'. This results in an error message. This time it is not a syntax error; the program fragment is syntactically correct. Instead, the error occurred while the program was running. The Traceback message indicates which line the error occurred on (line 1 of "standard input"). It is followed by the name of the error, IndexError, and a brief explanation.

In general, how do we know what we can index up to? If we know the length of the string is n, the highest valid index will be n-1. We can get access to the length of the string using the built-in len() function.

>>> len(msg)
11
>>>

Informally, a function is a named snippet of code that provides a service to our program when we call or execute it by name. We call the len() function by putting parentheses after the name and giving it the string msg we want to know the length of. Because len() is built into the Python interpreter, IDLE colors it purple.

>>> msg[-1]
'd'
>>>

>>> msg[-3]
'r'
>>> msg[-6]
' '
>>>

Now the computer works out the location in memory relative to the string's address plus its length, subtracting the index, e.g. 3136 + 11 - 1 = 3146. We can also visualize negative indices as shown in Figure 1.4.

Figure 1.4: Negative Indices

Thus we have two ways to access the characters in a string, from the start or the end. For example, we can access the space in the middle of Hello and World with either msg[5] or msg[-6]; these refer to the same location, because 5 = len(msg) - 6.

1.4.2   Accessing Substrings

In NLP we usually want to access more than one character at a time. This is also pretty simple; we just need to specify a start and end index. For example, the following code accesses the substring starting at index 1, up to (but not including) index 4:

>>> msg[1:4]
'ell'
>>>

The notation :4 is known as a slice. Here we see the characters are 'e', 'l' and 'l' which correspond to msg[1], msg[2] and msg[3], but not msg[4]. This is because a slice starts at the first index but finishes one before the end index. This is consistent with indexing: indexing also starts from zero and goes up to one before the length of the string. We can see this by slicing with the value of len():

>>> len(msg)
11
>>> msg[0:11]
'Hello World'
>>>

>>> msg[0:-6]
'Hello'
>>>

>>> msg[:3]
'Hel'
>>> msg[6:]
'World'
>>>

1.5.1   Lists

A list is designed to store a sequence of values. A list is similar to a string in many ways except that individual items don't have to be just characters; they can be arbitrary strings, integers or even other lists.

>>> squares = [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
>>> shopping_list = ['juice', 'muffins', 'bleach', 'shampoo']

>>> cgi = ['colorless', 'green', 'ideas']
>>> cgi
['colorless', 'green', 'ideas']
>>>

>>> len(cgi)
3
>>> cgi[0]
'colorless'
>>> cgi[-1]
'ideas'
>>> cgi[-5]
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
IndexError: list index out of range
>>>

>>> cgi[1:3]
['green', 'ideas']
>>> cgi[-2:]
['green', 'ideas']
>>>

>>> chomsky = cgi + ['sleep', 'furiously']
>>> chomsky
['colorless', 'green', 'ideas', 'sleep', 'furiously']
>>> cgi
['colorless', 'green', 'ideas']
>>>

>>> cgi[0] = 'colorful'
>>> cgi
['colorful', 'green', 'ideas']
>>>

>>> msg[0] = 'J'
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
TypeError: object does not support item assignment
>>>

This is because strings are immutable — you can't change a string once you have created it. However, lists are mutable, and their contents can be modified at any time. As a result, lists support a number of operations, or methods, that modify the original value rather than returning a new value. A method is a function that is associated with a particular object. A method is called on the object by giving the object's name, then a period, then the name of the method, and finally the parentheses containing any arguments. For example, in the following code we use the sort() and reverse() methods:

>>> chomsky.sort()
>>> chomsky.reverse()
>>> chomsky
['sleep', 'ideas', 'green', 'furiously', 'colorless']
>>>

>>> chomsky.append('said')
>>> chomsky.append('Chomsky')
>>> chomsky
['sleep', 'ideas', 'green', 'furiously', 'colorless', 'said', 'Chomsky']
>>> chomsky.index('green')
2
>>>

Finally, just as a reminder, you can create lists of any values you like. As you can see in the following example for a lexical entry, the values in a list do not even have to have the same type (though this is usually not a good idea, as we will explain in Section 5.2).

>>> bat = ['bat', [[1, 'n', 'flying mammal'], [2, 'n', 'striking instrument']]]
>>>

1.5.2   Working on Sequences One Item at a Time

We have shown you how to create lists, and how to index and manipulate them in various ways. Often it is useful to step through a list and process each item in some way. We do this using a for loop. This is our first example of a control structure in Python, a statement that controls how other statements are run:

>>> for num in [1, 2, 3]:
...     print 'The number is', num
...
The number is 1
The number is 2
The number is 3

>>> chomsky = ['colorless', 'green', 'ideas', 'sleep', 'furiously']
>>> for word in chomsky:
...     print len(word), word[-1], word
...
9 s colorless
5 n green
5 s ideas
5 p sleep
9 y furiously

The first time through this loop, the variable is assigned the value 'colorless'. This program runs the statement print len(word), word[-1], word for this value, to produce the output line: 9 s colorless. This process is known as iteration. Each iteration of the for loop starts by assigning the next item of the list chomsky to the loop variable word. Then the indented body of the loop is run. Here the body consists of a single command, but in general the body can contain as many lines of code as you want, so long as they are all indented by the same amount. (We recommend that you always use exactly 4 spaces for indentation, and that you never use tabs.)

We can run another for loop over the Chomsky nonsense sentence, and calculate the average word length. As you will see, this program uses the len() function in two ways: to count the number of characters in a word, and to count the number of words in a phrase. Note that x += y is shorthand for x = x + y; this idiom allows us to increment the total variable each time the loop is run.

>>> total = 0
>>> for word in chomsky:
...     total += len(word)
...
>>> total / len(chomsky)
6
>>>

>>> sent = 'colorless green ideas sleep furiously'
>>> for char in sent:
...     print char,
...
c o l o r l e s s   g r e e n   i d e a s   s l e e p   f u r i o u s l y
>>>

>>> for foo123 in 'colorless green ideas sleep furiously':
...     print foo123,
...
c o l o r l e s s   g r e e n   i d e a s   s l e e p   f u r i o u s l y
>>> for foo123 in ['colorless', 'green', 'ideas', 'sleep', 'furiously']:
...     print foo123,
...
colorless green ideas sleep furiously
>>>

>>> for word in chomsky:
...     print word, '(', len(word), '),',
colorless ( 9 ), green ( 5 ), ideas ( 5 ), sleep ( 5 ), furiously ( 9 ),
>>>

However, this approach has a couple of problems. First, the print statement intermingles variables and punctuation, making it a little difficult to read. Second, the output has spaces around every item that was printed. A cleaner way to produce structured output uses Python's string formatting expressions. Before diving into clever formatting tricks, however, let's look at a really simple example. We are going to use a special symbol, %s, as a placeholder in strings. Once we have a string containing this placeholder, we follow it with a single % and then a value v. Python then returns a new string where v has been slotted in to replace %s:

>>> "I want a %s right now" % "coffee"
'I want a coffee right now'
>>>

>>> "%s wants a %s %s" % ("Lee", "sandwich", "for lunch")
'Lee wants a sandwich for lunch'
>>>

>>> menu = ['sandwich', 'spam fritter', 'pancake']
>>> for snack in menu:
...     "Lee wants a %s right now" % snack
...
'Lee wants a sandwich right now'
'Lee wants a spam fritter right now'
'Lee wants a pancake right now'
>>>

We oversimplified things when we said that placeholders were of the form %s; in fact, this is a complex object, called a conversion specifier. This has to start with the % character, and ends with conversion character such as s` or ``d. The %s specifier tells Python that the corresponding variable is a string (or should be converted into a string), while the %d specifier indicates that the corresponding variable should be converted into a decimal representation. The string containing conversion specifiers is called a format string.

>>> for word in chomsky:
...     print "%s (%d)," % (word, len(word)),
colorless (9), green (5), ideas (5), sleep (5), furiously (9),
>>>

To summarize, string formatting is accomplished with a three-part object having the syntax: format % values. The format section is a string containing format specifiers such as %s and %d that Python will replace with the supplied values. The values section of a formatting string is a parenthesized list containing exactly as many items as there are format specifiers in the format section. In the case that there is just one item, the parentheses can be left out. (We will discuss Python's string-formatting expressions in more detail in Section 5.3.2).

In the above example, we used a trailing comma to suppress the printing of a newline. Suppose, on the other hand, that we want to introduce some additional newlines in our output. We can accomplish this by inserting the "special" character \n into the print string:

>>> for word in chomsky:
...    print "Word = %s\nIndex = %s\n*****" % (word, chomsky.index(word))
...
Word = colorless
Index = 0
*****
Word = green
Index = 1
*****
Word = ideas
Index = 2
*****
Word = sleep
Index = 3
*****
Word = furiously
Index = 4
*****
>>>

1.5.4   Converting Between Strings and Lists

Often we want to convert between a string containing a space-separated list of words and a list of strings. Let's first consider turning a list into a string. One way of doing this is as follows:

>>> s = ''
>>> for word in chomsky:
...    s += ' ' + word
...
>>> s
' colorless green ideas sleep furiously'
>>>

One drawback of this approach is that we have an unwanted space at the start of s. It is more convenient to use the join() method. We specify the string to be used as the "glue", followed by a period, followed by the join() function.

>>> sent = ' '.join(chomsky)
>>> sent
'colorless green ideas sleep furiously'
>>>

So ' '.join(chomsky) means: take all the items in chomsky and concatenate them as one big string, using ' ' as a spacer between the items.

Now let's try to reverse the process: that is, we want to convert a string into a list. Again, we could start off with an empty list [] and append() to it within a for loop. But as before, there is a more succinct way of achieving the same goal. This time, we will split the new string sent on whitespace:

To consolidate your understanding of joining and splitting strings, let's try the same thing using a semicolon as the separator:

>>> sent = ';'.join(chomsky)
>>> sent
'colorless;green;ideas;sleep;furiously'
>>> sent.split(';')
['colorless', 'green', 'ideas', 'sleep', 'furiously']
>>>

To be honest, many people find the notation for join() rather unintuitive. There is another function for converting lists to strings, again called join() which is called directly on the list. It uses whitespace by default as the "glue". However, we need to explicitly import this function into our code. One way of doing this is as follows:

>>> import string
>>> string.join(chomsky)
'colorless green ideas sleep furiously'
>>>

>>> string.join(chomsky, ';')
'colorless;green;ideas;sleep;furiously'
>>>

We will see other examples of statements with import later in this chapter. In general, we use import statements when we want to get access to Python code that doesn't already come as part of core Python. This code will exist somewhere as one or more files. Each such file corresponds to a Python module — this is a way of grouping together code and data that we regard as reusable. When you write down some Python statements in a file, you are in effect creating a new Python module. And you can make your code depend on another module by using the import statement. In our example earlier, we imported the module string and then used the join() function from that module. By adding string. to the beginning of join(), we make it clear to the Python interpreter that the definition of join() is given in the string module. An alternative, and equally valid, approach is to use the from module import identifier statement, as shown in the next example:

>>> from string import join
>>> join(chomsky)
'colorless green ideas sleep furiously'
>>>

Strings and lists are both kind of sequence. As such, they can both be indexed and sliced:

>>> query = 'Who knows?'
>>> beatles = ['john', 'paul', 'george', 'ringo']
>>> query[2]
'o'
>>> beatles[2]
'george'
>>> query[:2]
'Wh'
>>> beatles[:2]
['john', 'paul']
>>>

>>> newstring = query + " I don't"
>>> newlist = beatles + ['brian', 'george']

What's the difference between strings and lists as far as NLP is concerned? As we will see in Chapter 2, when we open a file for reading into a Python program, what we get initially is a string, corresponding to the contents of the whole file. If we try to use a for loop to process the elements of this string, all we can pick out are the individual characters in the string — we don't get to choose the granularity. By contrast, the elements of a list can be as big or small as we like: for example, they could be paragraphs, sentence, phrases, words, characters. So lists have this huge advantage, that we can be really flexible about the elements they contain, and correspondingly flexible about what the downstream processing will act on. So one of the first things we are likely to do in a piece of NLP code is convert a string into a list (of strings). Conversely, when we want to write our results to a file, or to a terminal, we will usually convert them to a string.

1.5.6   Exercises

1. ☼ Using the Python interactive interpreter, experiment with the examples in this section. Think of a sentence and represent it as a list of strings, e.g. ['Hello', 'world']. Try the various operations for indexing, slicing and sorting the elements of your list. Extract individual items (strings), and perform some of the string operations on them.

2. ☼ Split sent on some other character, such as 's'.

3. ☼ We pointed out that when phrase is a list, phrase.reverse() returns a modified version of phrase rather than a new list. On the other hand, we can use the slice trick mentioned in the exercises for the previous section, [::-1] to create a new reversed list without changing phrase. Show how you can confirm this difference in behavior.

4. ☼ We have seen how to represent a sentence as a list of words, where each word is a sequence of characters. What does phrase1[2][2] do? Why? Experiment with other index values.

5. ☼ Write a for loop to print out the characters of a string, one per line.

6. ☼ What is the difference between calling split on a string with no argument or with ' ' as the argument, e.g. sent.split() versus sent.split(' ')? What happens when the string being split contains tab characters, consecutive space characters, or a sequence of tabs and spaces? (In IDLE you will need to use '\t' to enter a tab character.)

7. ☼ Create a variable words containing a list of words. Experiment with words.sort() and sorted(words). What is the difference?

8. ☼ Earlier, we asked you to use a text editor to create a file called test.py, containing the single line msg = 'Hello World'. If you haven't already done this (or can't find the file), go ahead and do it now. Next, start up a new session with the Python interpreter, and enter the expression msg at the prompt. You will get an error from the interpreter. Now, try the following (note that you have to leave off the .py part of the filename):

   >>> from test import msg >>> msg

   This time, Python should return with a value. You can also try import test, in which case Python should be able to evaluate the expression test.msg at the prompt.

9. ◑ Process the list chomsky using a for loop, and store the result in a new list lengths. Hint: begin by assigning the empty list to lengths, using lengths = []. Then each time through the loop, use append() to add another length value to the list.

10. ◑ Define a variable silly to contain the string: 'newly formed bland ideas are inexpressible in an infuriating way'. (This happens to be the legitimate interpretation that bilingual English-Spanish speakers can assign to Chomsky's famous phrase, according to Wikipedia). Now write code to perform the following tasks:

    1. Split silly into a list of strings, one per word, using Python's split() operation, and save this to a variable called bland.
    2. Extract the second letter of each word in silly and join them into a string, to get 'eoldrnnnna'.
    3. Combine the words in bland back into a single string, using join(). Make sure the words in the resulting string are separated with whitespace.
    4. Print the words of silly in alphabetical order, one per line.

11. ◑ The index() function can be used to look up items in sequences. For example, 'inexpressible'.index('e') tells us the index of the first position of the letter e.

    1. What happens when you look up a substring, e.g. 'inexpressible'.index('re')?
    2. Define a variable words containing a list of words. Now use words.index() to look up the position of an individual word.
    3. Define a variable silly as in the exercise above. Use the index() function in combination with list slicing to build a list phrase consisting of all the words up to (but not including) in in silly.

1.6.1   Making Simple Decisions

Most programming languages permit us to execute a block of code when a conditional expression, or if statement, is satisfied. In the following program, we have created a variable called word containing the string value 'cat'. The if statement then checks whether the condition len(word) < 5 is true. Because the conditional expression is true, the body of the if statement is invoked and the print statement is executed.

>>> word = "cat"
>>> if len(word) < 5:
...   print 'word length is less than 5'
...
word length is less than 5
>>>

>>> if len(word) >= 5:
...   print 'word length is greater than or equal to 5'
...
>>>

The if statement, just like the for statement above is a control structure. An if statement is a control structure because it controls whether the code in the body will be run. You will notice that both if and for have a colon at the end of the line, before the indentation begins. That's because all Python control structures end with a colon.

>>> if len(word) >= 5:
...   print 'word length is greater than or equal to 5'
... else:
...   print 'word length is less than 5'
...
word length is less than 5
>>>

>>> if len(word) < 3:
...   print 'word length is less than three'
... elif len(word) == 3:
...   print 'word length is equal to three'
... else:
...   print 'word length is greater than three'
...
word length is equal to three
>>>

>>> mixed = ['cat', '', ['dog'], []]
>>> for element in mixed:
...     if element:
...         print element
...
cat
['dog']

>>> animals = ['cat', 'dog']
>>> if 'cat' in animals:
...     print 1
... elif 'dog' in animals:
...     print 2
...
1
>>>

Python supports a wide range of operators like < and >= for testing the relationship between values. The full set of these relational operators are shown in Table 1.1.

Table 1.1

Operator	Relationship
<	less than
<=	less than or equal to
==	equal to (note this is two not one = sign)
!=	not equal to
>	greater than
>=	greater than or equal to

Conditional Expressions

Normally we use conditional expressions as part of an if statement. However, we can test these relational operators directly at the prompt:

>>> 3 < 5
True
>>> 5 < 3
False
>>> not 5 < 3
True
>>>

Here we see that these expressions have Boolean values, namely True or False. not is a Boolean operator, and flips the truth value of Boolean statement.

>>> word = 'sovereignty'
>>> 'sovereign' in word
True
>>> 'gnt' in word
True
>>> 'pre' not in word
True
>>> 'Hello' in ['Hello', 'World']
True
>>> 'Hell' in ['Hello', 'World']
False
>>>

>>> word.startswith('sovereign')
True
>>> word.endswith('ty')
True
>>>

Integers, strings and lists are all kinds of data types in Python, and have types int, str and list respectively. In fact, every value in Python has a type. Python's type() function will tell you what an object's type is:

>>> oddments = ['cat', 'cat'.index('a'), 'cat'.split()]
>>> for e in oddments:
...     type(e)
...
<type 'str'>
<type 'int'>
<type 'list'>
>>>

>>> one = 'cat'
>>> one[0]
'c'
>>> two = [1, 2, 3]
>>> two[1]
2
>>> three = 1234
>>> three[2]
Traceback (most recent call last):
  File "<pyshell#95>", line 1, in -toplevel-
    three[2]
TypeError: 'int' object is unsubscriptable
>>>

>>> query = 'Who knows?'
>>> beatles = ['john', 'paul', 'george', 'ringo']
>>> query + beatles
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: cannot concatenate 'str' and 'list' objects

>>> 'Hi' * 3
'HiHiHi'
>>> 'Hi' - 'i'
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for -: 'str' and 'str'
>>> 6 / 2
3
>>> 'Hi' / 2
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for /: 'str' and 'int'
>>>

1. ☼ Assign a new value to sentence, namely the string 'she sells sea shells by the sea shore', then write code to perform the following tasks:

   1. Print all words beginning with 'sh':
   2. Print all words longer than 4 characters.
   3. Generate a new sentence that adds the popular hedge word 'like' before every word beginning with 'se'. Your result should be a single string.

2. ☼ Write code to abbreviate text by removing all the vowels. Define sentence to hold any string you like, then initialize a new string result to hold the empty string ''. Now write a for loop to process the string, one character at a time, and append any non-vowel characters to the result string.

3. ☼ We pointed out that when empty strings and empty lists occur in the condition part of an if clause, they evaluate to false. In this case, they are said to be occuring in a Boolean context. Experiment with different kind of non-Boolean expressions in Boolean contexts, and see whether they evaluate as true or false.

4. ☼ Review conditional expressions, such as 'row' in 'brown' and 'row' in ['brown', 'cow'].

   1. Define sent to be the string 'colorless green ideas sleep furiously', and use conditional expressions to test for the presence of particular words or substrings.
   2. Now define words to be a list of words contained in the sentence, using sent.split(), and use conditional expressions to test for the presence of particular words or substrings.

5. ◑ Write code to convert text into hAck3r, where characters are mapped according to the following table:

   Table 1.2

   Input:	e	i	o	l	s	.	ate
   Output:	3	1	0	|	5	5w33t!	8

1.7.1   Accessing Data with Data

Python provides a dictionary data type that can be used for mapping between arbitrary types.

>>> pos = {}
>>> pos['colorless'] = 'adj'
>>> pos['furiously'] = 'adv'
>>> pos['ideas'] = 'n'
>>>

>>> pos['ideas']
'n'
>>> pos['colorless']
'adj'
>>>

The item used for look-up is called the key, and the data that is returned is known as the value. As with indexing a list or string, we get an exception when we try to access the value of a key that does not exist:

>>> pos['missing']
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
KeyError: 'missing'
>>>

>>> 'colorless' in pos
True
>>> 'missing' in pos
False
>>> 'missing' not in pos
True
>>>

>>> for word in pos:
...     print "%s (%s)" % (word, pos[word])
...
colorless (adj)
furiously (adv)
ideas (n)
>>>

>>> pos
{'furiously': 'adv', 'ideas': 'n', 'colorless': 'adj'}
>>>

Here, the contents of the dictionary are shown as key-value pairs. As you can see, the order of the key-value pairs is different from the order in which they were originally entered. This is because dictionaries are not sequences but mappings. The keys in a mapping are not inherently ordered, and any ordering that we might want to impose on the keys exists independently of the mapping. As we shall see later, this gives us a lot of flexibility.

>>> pos = {'furiously': 'adv', 'ideas': 'n', 'colorless': 'adj'}
>>>

>>> pos.keys()
['colorless', 'furiously', 'ideas']
>>> pos.values()
['adj', 'adv', 'n']
>>> pos.items()
[('colorless', 'adj'), ('furiously', 'adv'), ('ideas', 'n')]
>>> for (key, val) in pos.items():
...     print "%s ==> %s" % (key, val)
...
colorless ==> adj
furiously ==> adv
ideas ==> n
>>>

>>> pos['content'] = 'n'
>>> pos['content'] = 'v'
>>> pos
{'content': 'v', 'furiously': 'adv', 'ideas': 'n', 'colorless': 'adj'}
>>>

The values stored in a dictionary can be any kind of object, not just a string — the values can even be dictionaries. The most common kind is actually an integer. It turns out that we can use a dictionary to store counters for many kinds of data. For instance, we can have a counter for all the letters of the alphabet; each time we get a certain letter we increment its corresponding counter:

>>> phrase = 'colorless green ideas sleep furiously'
>>> count = {}
>>> for letter in phrase:
...     if letter not in count:
...         count[letter] = 0
...     count[letter] += 1
>>> count
{'a': 1, ' ': 4, 'c': 1, 'e': 6, 'd': 1, 'g': 1, 'f': 1, 'i': 2,
 'l': 4, 'o': 3, 'n': 1, 'p': 1, 's': 5, 'r': 3, 'u': 2, 'y': 1}
>>>

>>> phrase = 'colorless green ideas sleep furiously'
>>> from nltk import defaultdict
>>> count = defaultdict(int)
>>> for letter in phrase:
...     count[letter] += 1
>>> count
{'a': 1, ' ': 4, 'c': 1, 'e': 6, 'd': 1, 'g': 1, 'f': 1, 'i': 2,
 'l': 4, 'o': 3, 'n': 1, 'p': 1, 's': 5, 'r': 3, 'u': 2, 'y': 1}
>>>

>>> sorted(count.items())
[(' ', 4), ('a', 1), ('c', 1), ('d', 1), ('e', 6), ('f', 1), ...,
...('y', 1)]
>>>

>>> sentence = "she sells sea shells by the sea shore".split()
>>> words = []
>>> for word in sentence:
...     if word not in words:
...         words.append(word)
...
>>> sorted(words)
['by', 'sea', 'sells', 'she', 'shells', 'shore', 'the']
>>>

There is a better way to do this task using Python's set data type. We can convert sentence into a set, using set(sentence):

>>> set(sentence)
set(['shells', 'sells', 'shore', 'she', 'sea', 'the', 'by'])
>>>

>>> sentence = "she sells sea shells by the sea shore".split()
>>> sorted(set(sentence))
['by', 'sea', 'sells', 'she', 'shells', 'shore', 'the']

>>> import nltk
>>> count = nltk.defaultdict(int)                     # initialize a dictionary
>>> for word in nltk.corpus.gutenberg.words('shakespeare-macbeth.txt'): # tokenize Macbeth
...     word = word.lower()                           # normalize to lowercase
...     count[word] += 1                              # increment the counter
...
>>>

You will learn more about accessing corpora in Section 2.2.3. For now, you just need to know that gutenberg.words() returns a list of words, in this case from Shakespeare's play Macbeth, and we are iterating over this list using a for loop. We convert each word to lowercase using the string method word.lower(), and use a dictionary to maintain a set of counters, one per word. Now we can inspect the contents of the dictionary to get counts for particular words:

>>> count['scotland']
12
>>> count['the']
692
>>>

1.7.5   Exercises

1. ☺ Review the mappings in Table 1.3. Discuss any other examples of mappings you can think of. What type of information do they map from and to?
2. ☼ Using the Python interpreter in interactive mode, experiment with the examples in this section. Create a dictionary d, and add some entries. What happens if you try to access a non-existent entry, e.g. d['xyz']?
3. ☼ Try deleting an element from a dictionary, using the syntax del d['abc']. Check that the item was deleted.
4. ☼ Create a dictionary e, to represent a single lexical entry for some word of your choice. Define keys like headword, part-of-speech, sense, and example, and assign them suitable values.
5. ☼ Create two dictionaries, d1 and d2, and add some entries to each. Now issue the command d1.update(d2). What did this do? What might it be useful for?
6. ◑ Write a program that takes a sentence expressed as a single string, splits it and counts up the words. Get it to print out each word and the word's frequency, one per line, in alphabetical order.

Returning briefly to our earlier problem with unwanted whitespace around three-letter words, we note that re.findall() behaves slightly differently if we create groups in the regular expression using parentheses; it only returns strings that occur within the groups:

>>> re.findall('\s(\w\w\w)\s', s)
['the', 'the', 'its', 'the', 'and', 'you']
>>>

>>> re.findall('([^aeiou])[aeiou]', sent)
['c', 'l', 'l', 'r', ' ', 'd', 'l', 'f', 'r']
>>>

By delimiting a second group in the regular expression, we can even generate pairs (or tuples) that we may then go on and tabulate.

>>> re.findall('([^aeiou])([aeiou])', sent)
[('c', 'o'), ('l', 'o'), ('l', 'e'), ('r', 'e'), (' ', 'i'),
    ('d', 'e'), ('l', 'e'), ('f', 'u'), ('r', 'i')]
>>>

>>> s3 = """
... <hart@vmd.cso.uiuc.edu>
... Final editing was done by Martin Ward <Martin.Ward@uk.ac.durham>
... Michael S. Hart <hart@pobox.com>
... Prepared by David Price, email <ccx074@coventry.ac.uk>"""

>>> re.findall(r'<(.+)@(.+)>', s3)
[('hart', 'vmd.cso.uiuc.edu'), ('Martin.Ward', 'uk.ac.durham'),
('hart', 'pobox.com'), ('ccx074', 'coventry.ac.uk')]
>>>

>>> re.findall(r'(\w+\.)', s3)
['vmd.', 'cso.', 'uiuc.', 'Martin.', 'uk.', 'ac.', 'S.',
'pobox.', 'coventry.', 'ac.']
>>>

>>> re.findall('(G|g)oogle', s)
['G', 'G', 'G', 'g']
>>>

>>> re.findall('(?:G|g)oogle', s)
['Google', 'Google', 'Google', 'google']
>>>

• Josh Cogliati, Non-Programmer's Tutorial for Python, http://en.wikibooks.org/wiki/Non-Programmer's_Tutorial_for_Python/Contents
• Allen B. Downey, Jeffrey Elkner and Chris Meyers, How to Think Like a Computer Scientist: Learning with Python, http://www.ibiblio.org/obp/thinkCSpy/

An Introduction to Python [Rossum & Jr., 2006] is a Python tutorial by Guido van Rossum, the inventor of Python and Fred L. Drake, Jr., the official editor of the Python documentation. It is available online at http://docs.python.org/tut/tut.html. A more detailed but still introductory text is [Lutz & Ascher, 2003], which covers the essential features of Python, and also provides an overview of the standard libraries.

[Beazley, 2006] is a succinct reference book; although not suitable as an introduction to Python, it is an excellent resource for intermediate and advanced programmers.

Finally, it is always worth checking the official Python Documentation at http://docs.python.org/.

1.10.2   Regular Expressions

There are many references for regular expressions, both practical and theoretical. [Friedl, 2002] is a comprehensive and detailed manual in using regular expressions, covering their syntax in most major programming languages, including Python.

For an introductory tutorial to using regular expressions in Python with the re module, see A. M. Kuchling, Regular Expression HOWTO, http://www.amk.ca/python/howto/regex/.

Chapter 3 of [Mertz, 2003] provides a more extended tutorial on Python's facilities for text processing with regular expressions.

http://www.regular-expressions.info/ is a useful online resource, providing a tutorial and references to tools and other sources of information.