Python Text Processing with NLTK 2.0 Cookbook

Installation instructions for NLTK are available at http://www.nltk.org/download and the latest version as of this writing is 2.0b9. NLTK requires Python 2.4 or higher, but is not compatible with Python 3.0. The recommended Python version is 2.6.

Once you've installed NLTK, you'll also need to install the data by following the instructions at http://www.nltk.org/data. We recommend installing everything, as we'll be using a number of corpora and pickled objects. The data is installed in a data directory, which on Mac and Linux/Unix is usually /usr/share/nltk_data, or on Windows is C:\nltk_data. Make sure that tokenizers/punkt.zip is in the data directory and has been unpacked so that there's a file at tokenizers/punkt/english.pickle.

Finally, to run the code examples, you'll need to start a Python console. Instructions on how to do so are available at http://www.nltk.org/getting-started. For Mac with Linux/Unix users, you can open a terminal and type python.

Once NLTK is installed and you have a Python console running, we can start by creating a paragraph of text:

>>> para = "Hello World. It's good to see you. Thanks for buying this book."

Now we want to split para into sentences. First we need to import the sentence tokenization function, and then we can call it with the paragraph as an argument.

>>> from nltk.tokenize import sent_tokenize
>>> sent_tokenize(para)
['Hello World.', "It's good to see you.", 'Thanks for buying this book.']

So now we have a list of sentences that we can use for further processing.

sent_tokenize uses an instance of PunktSentenceTokenizer from the nltk.tokenize.punkt module. This instance has already been trained on and works well for many European languages. So it knows what punctuation and characters mark the end of a sentence and the beginning of a new sentence.

The instance used in sent_tokenize() is actually loaded on demand from a pickle file. So if you're going to be tokenizing a lot of sentences, it's more efficient to load the PunktSentenceTokenizer once, and call its tokenize() method instead.

>>> import nltk.data
>>> tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
>>> tokenizer.tokenize(para)
['Hello World.', "It's good to see you.", 'Thanks for buying this book.']

>>> spanish_tokenizer = nltk.data.load('tokenizers/punkt/spanish.pickle')
>>> spanish_tokenizer.tokenize('Hola amigo. Estoy bien.')

In the next recipe, we'll learn how to split sentences into individual words. After that, we'll cover how to use regular expressions for tokenizing text.

Basic word tokenization is very simple: use the word_tokenize() function:

>>> from nltk.tokenize import word_tokenize
>>> word_tokenize('Hello World.')
['Hello', 'World', '.']

word_tokenize() is a wrapper function that calls tokenize() on an instance of the TreebankWordTokenizer . It's equivalent to the following:

>>> from nltk.tokenize import TreebankWordTokenizer
>>> tokenizer = TreebankWordTokenizer()
>>> tokenizer.tokenize('Hello World.')
['Hello', 'World', '.']

It works by separating words using spaces and punctuation. And as you can see, it does not discard the punctuation, allowing you to decide what to do with it.

Ignoring the obviously named WhitespaceTokenizer and SpaceTokenizer , there are two other word tokenizers worth looking at: PunktWordTokenizer and WordPunctTokenizer. These differ from the TreebankWordTokenizer by how they handle punctuation and contractions, but they all inherit from TokenizerI. The inheritance tree looks like this:

>>> word_tokenize("can't")
['ca', "n't"]

>>> from nltk.tokenize import PunktWordTokenizer
>>> tokenizer = PunktWordTokenizer()
>>> tokenizer.tokenize("Can't is a contraction.")
['Can', "'t", 'is', 'a', 'contraction.']

>>> from nltk.tokenize import WordPunctTokenizer
>>> tokenizer = WordPunctTokenizer()
>>> tokenizer.tokenize("Can't is a contraction.")
['Can', "'", 't', 'is', 'a', 'contraction', '.']

For more control over word tokenization, you'll want to read the next recipe to learn how to use regular expressions and the RegexpTokenizer for tokenization.

First you need to decide how you want to tokenize a piece of text, as this will determine how you construct your regular expression. The choices are:

We'll start with an example of the first, matching alphanumeric tokens plus single quotes so that we don't split up contractions.

We'll create an instance of the RegexpTokenizer, giving it a regular expression string to use for matching tokens.

>>> from nltk.tokenize import RegexpTokenizer
>>> tokenizer = RegexpTokenizer("[\w']+")
>>> tokenizer.tokenize("Can't is a contraction.")
["Can't", 'is', 'a', 'contraction']

There's also a simple helper function you can use in case you don't want to instantiate the class.

>>> from nltk.tokenize import regexp_tokenize
>>> regexp_tokenize("Can't is a contraction.", "[\w']+")
["Can't", 'is', 'a', 'contraction']

Now we finally have something that can treat contractions as whole words, instead of splitting them into tokens.

The RegexpTokenizer works by compiling your pattern, then calling re.findall() on your text. You could do all this yourself using the re module, but the RegexpTokenizer implements the TokenizerI interface, just like all the word tokenizers from the previous recipe. This means it can be used by other parts of the NLTK package, such as corpus readers, which we'll cover in detail in Chapter 3, Creating Custom Corpora. Many corpus readers need a way to tokenize the text they're reading, and can take optional keyword arguments specifying an instance of a TokenizerI subclass. This way, you have the ability to provide your own tokenizer instance if the default tokenizer is unsuitable.

RegexpTokenizer can also work by matching the gaps, instead of the tokens. Instead of using re.findall(), the RegexpTokenizer will use re.split(). This is how the BlanklineTokenizer in nltk.tokenize is implemented.

>>> tokenizer = RegexpTokenizer('\s+', gaps=True)
>>> tokenizer.tokenize("Can't is a contraction.")
 ["Can't", 'is', 'a', 'contraction.']

For simpler word tokenization, see the previous recipe.

NLTK comes with a stopwords corpus that contains word lists for many languages. Be sure to unzip the datafile so NLTK can find these word lists in nltk_data/corpora/stopwords/.

We're going to create a set of all English stopwords, then use it to filter stopwords from a sentence.

>>> from nltk.corpus import stopwords
>>> english_stops = set(stopwords.words('english'))
>>> words = ["Can't", 'is', 'a', 'contraction']
>>> [word for word in words if word not in english_stops]
["Can't", 'contraction']

The stopwords corpus is an instance of nltk.corpus.reader.WordListCorpusReader. As such, it has a words() method that can take a single argument for the file ID, which in this case is 'english', referring to a file containing a list of English stopwords. You could also call stopwords.words() with no argument to get a list of all stopwords in every language available.

You can see the list of all English stopwords using stopwords.words('english') or by examining the word list file at nltk_data/corpora/stopwords/english. There are also stopword lists for many other languages. You can see the complete list of languages using the fileids() method:

>>> stopwords.fileids()
['danish', 'dutch', 'english', 'finnish', 'french', 'german', 'hungarian', 'italian', 'norwegian', 'portuguese', 'russian', 'spanish', 'swedish', 'turkish']

Any of these fileids can be used as an argument to the words() method to get a list of stopwords for that language.

If you'd like to create your own stopwords corpus, see the Creating a word list corpus recipe in Chapter 3, Creating Custom Corpora, to learn how to use the WordListCorpusReader. We'll also be using stopwords in the Discovering word collocations recipe, later in this chapter.

Be sure you've unzipped the wordnet corpus in nltk_data/corpora/wordnet. This will allow the WordNetCorpusReader to access it.

Now we're going to lookup the synset for cookbook, and explore some of the properties and methods of a synset.

>>> from nltk.corpus import wordnet
>>> syn = wordnet.synsets('cookbook')[0]
>>> syn.name
'cookbook.n.01'
>>> syn.definition
'a book of recipes and cooking directions'

You can look up any word in WordNet using wordnet.synsets(word) to get a list of synsets. The list may be empty if the word is not found. The list may also have quite a few elements, as some words can have many possible meanings and therefore many synsets.

Each synset in the list has a number of attributes you can use to learn more about it. The name attribute will give you a unique name for the synset, which you can use to get the synset directly.

>>> wordnet.synset('cookbook.n.01')
Synset('cookbook.n.01')

The definition attribute should be self-explanatory. Some synsets also have an examples attribute, which contains a list of phrases that use the word in context.

>>> wordnet.synsets('cooking')[0].examples
['cooking can be a great art', 'people are needed who have experience in cookery', 'he left the preparation of meals to his wife']

>>> syn.hypernyms()
[Synset('reference_book.n.01')]
>>> syn.hypernyms()[0].hyponyms()
[Synset('encyclopedia.n.01'), Synset('directory.n.01'), Synset('source_book.n.01'), Synset('handbook.n.01'), Synset('instruction_book.n.01'), Synset('cookbook.n.01'), Synset('annual.n.02'), Synset('atlas.n.02'), Synset('wordbook.n.01')]
>>> syn.root_hypernyms()
[Synset('entity.n.01')]

>>> syn.hypernym_paths()
[[Synset('entity.n.01'), Synset('physical_entity.n.01'), Synset('object.n.01'), Synset('whole.n.02'), Synset('artifact.n.01'), Synset('creation.n.02'), Synset('product.n.02'), Synset('work.n.02'), Synset('publication.n.01'), Synset('book.n.01'), Synset('reference_book.n.01'), Synset('cookbook.n.01')]]

>>> syn.pos
'n'

>>> len(wordnet.synsets('great'))
7
>>> len(wordnet.synsets('great', pos='n'))
1
>>> len(wordnet.synsets('great', pos='a'))
6

In the next two recipes, we'll explore lemmas and how to calculate synset similarity. In Chapter 2, Replacing and Correcting Words, we'll use WordNet for lemmatization, synonym replacement, and then explore the use of antonyms.

In the following block of code, we'll find that there are two lemmas for the cookbook synset by using the lemmas attribute:

>>> from nltk.corpus import wordnet
>>> syn = wordnet.synsets('cookbook')[0]
>>> lemmas = syn.lemmas
>>> len(lemmas)
2
>>> lemmas[0].name
'cookbook'
>>> lemmas[1].name
'cookery_book'
>>> lemmas[0].synset == lemmas[1].synset
True

As you can see, cookery_book and cookbook are two distinct lemmas in the same synset. In fact, a lemma can only belong to a single synset. In this way, a synset represents a group of lemmas that all have the same meaning, while a lemma represents a distinct word form.

Since lemmas in a synset all have the same meaning, they can be treated as synonyms. So if you wanted to get all synonyms for a synset, you could do:

>>> [lemma.name for lemma in syn.lemmas]
['cookbook', 'cookery_book']

>>> synonyms = []
>>> for syn in wordnet.synsets('book'):
...     for lemma in syn.lemmas:
...         synonyms.append(lemma.name)
>>> len(synonyms)
38

>>> len(set(synonyms))
25

>>> gn2 = wordnet.synset('good.n.02')
>>> gn2.definition
'moral excellence or admirableness'
>>> evil = gn2.lemmas[0].antonyms()[0]
>>> evil.name
'evil'
>>> evil.synset.definition
'the quality of being morally wrong in principle or practice'
>>> ga1 = wordnet.synset('good.a.01')
>>> ga1.definition
'having desirable or positive qualities especially those suitable for a thing specified'
>>> bad = ga1.lemmas[0].antonyms()[0]
>>> bad.name
'bad'
>>> bad.synset.definition
'having undesirable or negative qualities'

In the next recipe, we'll learn how to calculate synset similarity. Then in Chapter 2, Replacing and Correcting Words, we'll revisit lemmas for lemmatization, synonym replacement, and antonym replacement.

If you were to look at all the hyponyms of reference book (which is the hypernym of cookbook) you'd see that one of them is instruction_book. These seem intuitively very similar to cookbook, so let's see what WordNet similarity has to say about it.

>>> from nltk.corpus import wordnet
>>> cb = wordnet.synset('cookbook.n.01')
>>> ib = wordnet.synset('instruction_book.n.01')
>>> cb.wup_similarity(ib)
0.91666666666666663

So they are over 91% similar!

wup_similarity is short for Wu-Palmer Similarity, which is a scoring method based on how similar the word senses are and where the synsets occur relative to each other in the hypernym tree. One of the core metrics used to calculate similarity is the shortest path distance between the two synsets and their common hypernym.

>>> ref = cb.hypernyms()[0]
>>> cb.shortest_path_distance(ref)
1
>>> ib.shortest_path_distance(ref)
1
>>> cb.shortest_path_distance(ib)
2

So cookbook and instruction book must be very similar, because they are only one step away from the same hypernym, reference book, and therefore only two steps away from each other.

Let's look at two dissimilar words to see what kind of score we get. We'll compare dog with cookbook, two seemingly very different words.

>>> dog = wordnet.synsets('dog')[0]
>>> dog.wup_similarity(cb)
0.38095238095238093

Wow, dog and cookbook are apparently 38% similar! This is because they share common hypernyms farther up the tree.

>>> dog.common_hypernyms(cb)
[Synset('object.n.01'), Synset('whole.n.02'), Synset('physical_entity.n.01'), Synset('entity.n.01')]

>>> cook = wordnet.synset('cook.v.01')
>>> bake = wordnet.synset('bake.v.02')
>>> cook.wup_similarity(bake)
0.75

>>> cb.path_similarity(ib)
0.33333333333333331
>>> cb.path_similarity(dog)
0.071428571428571425
>>> cb.lch_similarity(ib)
2.5389738710582761
>>> cb.lch_similarity(dog)
0.99852883011112725

The recipe on Looking up synsets for a word in WordNet, discussed earlier in this chapter, has more details about hypernyms and the hypernym tree.

The script for Monty Python and the Holy Grail is found in the webtext corpus, so be sure that it's unzipped in nltk_data/corpora/webtext/.

We're going to create a list of all lowercased words in the text, and then produce a BigramCollocationFinder, which we can use to find bigrams, which are pairs of words. These bigrams are found using association measurement functions found in the nltk.metrics package.

>>> from nltk.corpus import webtext
>>> from nltk.collocations import BigramCollocationFinder
>>> from nltk.metrics import BigramAssocMeasures
>>> words = [w.lower() for w in webtext.words('grail.txt')]
>>> bcf = BigramCollocationFinder.from_words(words)
>>> bcf.nbest(BigramAssocMeasures.likelihood_ratio, 4)
[("'", 's'), ('arthur', ':'), ('#', '1'), ("'", 't')]

Well that's not very useful! Let's refine it a bit by adding a word filter to remove punctuation and stopwords.

>>> from nltk.corpus import stopwords
>>> stopset = set(stopwords.words('english'))
>>> filter_stops = lambda w: len(w) < 3 or w in stopset
>>> bcf.apply_word_filter(filter_stops)
>>> bcf.nbest(BigramAssocMeasures.likelihood_ratio, 4)
[('black', 'knight'), ('clop', 'clop'), ('head', 'knight'), ('mumble', 'mumble')]

Much better—we can clearly see four of the most common bigrams in Monty Python and the Holy Grail. If you'd like to see more than four, simply increase the number to whatever you want, and the collocation finder will do its best.

The BigramCollocationFinder constructs two frequency distributions: one for each word, and another for bigrams. A frequency distribution, or FreqDist in NLTK, is basically an enhanced dictionary where the keys are what's being counted, and the values are the counts. Any filtering functions that are applied, reduce the size of these two FreqDists by eliminating any words that don't pass the filter. By using a filtering function to eliminate all words that are one or two characters, and all English stopwords, we can get a much cleaner result. After filtering, the collocation finder is ready to accept a generic scoring function for finding collocations. Additional scoring functions are covered in the Scoring functions section further in this chapter.

In addition to BigramCollocationFinder, there's also TrigramCollocationFinder, for finding triples instead of pairs. This time, we'll look for trigrams in Australian singles ads.

>>> from nltk.collocations import TrigramCollocationFinder
>>> from nltk.metrics import TrigramAssocMeasures
>>> words = [w.lower() for w in webtext.words('singles.txt')]
>>> tcf = TrigramCollocationFinder.from_words(words)
>>> tcf.apply_word_filter(filter_stops)
>>> tcf.apply_freq_filter(3)
>>> tcf.nbest(TrigramAssocMeasures.likelihood_ratio, 4)
[('long', 'term', 'relationship')]

Now, we don't know whether people are looking for a long-term relationship or not, but clearly it's an important topic. In addition to the stopword filter, we also applied a frequency filter which removed any trigrams that occurred less than three times. This is why only one result was returned when we asked for four—because there was only one result that occurred more than twice.

The nltk.metrics module will be used again in Chapter 7, Text Classification.