## Welcome to Natural Language Processing in Python (Part 2)

If you have not seen Part 1 of this tutorial, please refer to the following link for information on how to install the Natural Language Toolkit in Python installed.

The companion video to this post on NLP can be viewed here:

The primary goals of this post will be to:

1. Understand a few terms you may be unfamiliar with from natural languge processing.

2. Be able to take full advantage of the text corpus provided from NLTK.

## A few NLP terms

Before going into what the bulk of this post will be focused on, let us briefly mention a couple natural language processing terms and some corresponding examples of each term.

Refer to Part 1 where this syntax is explained in greater detail. We will continue to use Lewis Carroll’s “Alice in Wonderland” as our primary exploratory text for NLP.

from nltk.text import Text
alice = Text(nltk.corpus.gutenberg.words('carroll-alice.txt'))
fdist = nltk.FreqDist(alice)


### Hapaxes

A hapax is a word that occurs only once within a context in a written text.

print(fdist.hapaxes())


### Collocations

A collocation is a pair or group of words that are habitually juxtaposed. For instance “red wine”, or in the context of “Alice in Wonderland”, pairs such as “White Rabbit” and “Red Queen” would be collocations.

print(alice.collocations())

Mock Turtle; said Alice; March Hare; White Rabbit; thought Alice;
golden key; beautiful Soup; white kid; good deal; kid gloves; Mary
Ann; yer honour; three gardeners; play croquet; Lobster Quadrille;
ootiful Soo; great hurry; old fellow; trembling voice; poor little


## Accessing NLTK Text Resources

Recall in Part 1 of this tutorial series, we ran the following command:

nltk.download()


This command was responsible for downloading various collections of text that we can use to run various NLP functions on. Thus far, we have made use of the Gutenberg collection of text to read in “Alice in Wonderland”.

### Gutenberg Corpus

There are a few more things to note about how one may access the Gutenberg data.

As we did in Part 1, it is possible to extract the words from a text read in via Gutenberg. For instance:

alice_words = nltk.corpus.gutenberg.words('carroll-alice.txt')


As we saw previously, this provides to us the words of, in this case, “Alice in Wonderland”. One may print out the words of this text by using Python’s print function:

print(alice_words)

['[', 'Alice', "'", 's', 'Adventures', 'in', ...]


Note that Python does not print out the entire list or words. The ellipsis (…) sequence denotes that there is more content that is supressed from output. In addition to extracting individual words, we may also extract characters and sentences. This may be accomplished using the following respective lines:

alice_chars = nltk.corpus.gutenberg.raw('carroll-alice.txt')
alice_sents = nltk.corpus.gutenberg.sents('carroll-alice.txt')


Now that we have access to the words, characters, and sentences of “Alice in Wonderland”, we may run a few rudimentary statistics on the text based on this information. For instance, calculating the average word length would simply amount to dividing the total number of characters by the total number of words:

print(int(len(alice_chars) / len(alice_words)))

4


In a similar manner, we may calculate the average sentence length by dividing the total number of words by the total number of sentences:

print(int(len(alice_words) / len(alice_sents)))

20


Let us turn the above two metrics into functions, and determine the average word length and sentence length of all the texts in the Gutenberg collection.

def avg_word_len(num_chars, num_words):
return int(num_chars/num_words)

def avg_sent_len(num_words, num_sents):
return int(num_words/num_sents)


Let us make use of these functions on the text that the NLTK gutenberg module provides to us. That is, we shall loop through each file provided via the gutenberg module, calculate the total number of chars, words, and sentences for each piece of work, and then display the average word length and average sentence length.

for file_id in nltk.corpus.gutenberg.fileids():
num_chars = len(nltk.corpus.gutenberg.raw(file_id))
num_words = len(nltk.corpus.gutenberg.words(file_id))
num_sents = len(nltk.corpus.gutenberg.sents(file_id))

print(file_id +
" has an average word length of " +
str(avg_word_len(num_chars, num_words)) +
" and an average sentence length of " +
str(avg_sent_len(num_words, num_sents)))

austen-emma.txt has an average word length of 4 and an average sentence length of 24
austen-persuasion.txt has an average word length of 4 and an average sentence length of 26
austen-sense.txt has an average word length of 4 and an average sentence length of 28
bible-kjv.txt has an average word length of 4 and an average sentence length of 33
blake-poems.txt has an average word length of 4 and an average sentence length of 19
bryant-stories.txt has an average word length of 4 and an average sentence length of 19
burgess-busterbrown.txt has an average word length of 4 and an average sentence length of 17
carroll-alice.txt has an average word length of 4 and an average sentence length of 20
chesterton-ball.txt has an average word length of 4 and an average sentence length of 20
chesterton-brown.txt has an average word length of 4 and an average sentence length of 22
chesterton-thursday.txt has an average word length of 4 and an average sentence length of 18
edgeworth-parents.txt has an average word length of 4 and an average sentence length of 20
melville-moby_dick.txt has an average word length of 4 and an average sentence length of 25
milton-paradise.txt has an average word length of 4 and an average sentence length of 52
shakespeare-caesar.txt has an average word length of 4 and an average sentence length of 11
shakespeare-hamlet.txt has an average word length of 4 and an average sentence length of 12
shakespeare-macbeth.txt has an average word length of 4 and an average sentence length of 12
whitman-leaves.txt has an average word length of 4 and an average sentence length of 36


Observe that the sentence length tends to vary, while the word length among all of these texts is consistent.

### Accessing Gutenberg Corpus via the Internet

Note that the gutenberg fileids only have a small subset of text compared to the large amount of content found on Project Gutenberg.

If you wish to process a text from Project Gutenberg accessed via the web, one may use the urllib module to import via the internet.

from urllib.request import urlopen

url = "https://www.gutenberg.org/cache/epub/174/pg174.txt"


The URL in the above example is a link to a text file consisting of Oscar Wilde’s “The Picture of Dorian Grey” hosted by Project Gutenberg. The above code navigates to that URL, reads the content on the page, and then converts it to a utf-8 formatted string.

Once the raw content has been extracted, we convert this content to something that NLTK can understand and process. This should look somewhat familiar if you have consulted Part 1 of this tutorial.

dorian_grey = nltk.Text(nltk.word_tokenize(raw))


Once the text has been converted to an NLTK Text object, we can process it just like we have been doing previously. For example, here we convert the text object to a frequency distribution and calculate the hapaxes.

fdist_dorian = nltk.FreqDist(dorian_grey)
print(fdist_dorian.hapaxes())


The above approach is not limited to text from Project Gutenberg, but is broadly applicable to any text that can be obtained from a direct URL.

### Web Text Corpus

Let us consider other text resource that NLTK allows us to process. One of them is various web and chat data. The first one we shall focus on his web text.

We can print out the file ids of the webtext collection to see what is provided:

for file_id in nltk.corpus.webtext.fileids():
print(file_id)

firefox.txt
grail.txt
overheard.txt
pirates.txt
singles.txt
wine.txt


We see a list of text files. For more information on the content of each of these file, you can consult: https://github.com/teropa/nlp/tree/master/resources/corpora/webtext

A very brief description of the content in the above link:

1. firefox.txt: Firefox support forum.

2. grail.txt: Movie script from “Monty Python and the Holy Grail”.

3. overheard.txt: Overheard conversation in New York.

4. pirates.txt: Movie script from Pirates of the Caribean.

6. wine.txt: “Fine Wine Diary” reviews.

Observe that many of the ways in which we access and processed text from gutenberg carry over into processing the webtext data. This is a common theme for all of the text resources provided by NLTK, and makes it easier to apply functionality for one text resource to another in a general fashion.

num_grail_words = len(nltk.corpus.webtext.words('grail.txt'))
num_grail_chars = len(nltk.corpus.webtext.raw('grail.txt'))
num_grail_sents = len(nltk.corpus.webtext.sents('grail.txt'))

print(avg_word_len(num_grail_chars, num_grail_words))
print(avg_sent_len(num_grail_words, num_grail_sents))

3
9


This is a collection of presidential inaugural addresses; the speech that the president makes prior to officially starting their term in office.

Let us print out the files provided to us via the inaugural corpus:

for file_id in nltk.corpus.inaugural.fileids():
prinf(file_id)

1789-Washington.txt
1793-Washington.txt
1801-Jefferson.txt
1805-Jefferson.txt
...


Each file consists of the format: X-Y, where X is the four digit year, and Y is the last name of the president giving the inaugural address.

Let us loop through each address. While doing so, let us keep a running tally of the number of times the word “America” is used in each address.

# Loop through each inaugural address:
for fileid in nltk.corpus.inaugural.fileids():
america_count = 0
# Loop through all words in current inaugural address:
for w in nltk.corpus.inaugural.words(fileid):
# We convert the word to lowercase before checking
# This makes checking for the occurrence more consistent.
# Note that the "startswith" function also catches words like
# "American", "Americans", etc.
if w.lower().startswith('america'):
america_count += 1
# Output both the inaugural address name and count for America:
president = fileid[5:-4]
year = fileid[:4]
print("President " + president +
" of year " + year +
" said America " + str(america_count) + " times. ")

President Washington of year 1789 said America 2 times.
President Washington of year 1793 said America 1 times.
President Adams of year 1797 said America 8 times.
President Jefferson of year 1801 said America 0 times.
President Jefferson of year 1805 said America 1 times.
President Madison of year 1809 said America 0 times.
...


Say I also want to see how many times the word “citizen” is present in each of the inaugural addresses. It may be preferable to consider a plot output as opposed to one that simply outputs to terminal.

Let us consider a conditional frequency distribution, that is, a frequency distribution that is a collection of frequency distributions run under different conditions.

Recall the FreqDist function took a list as input. NLTK provides a ConditionalFreqDist function as well which takes a list of pairs. Each pair has the form (condition, event).

In our example, we care about the case when either the word “America” or “citizen” is used in each of the inaugural addresses. In other words, encountering the phrase “America” or “citizen” are the conditions we care about, and the events are one for each year of the inaugural address.

cfd = nltk.ConditionalFreqDist(
(target, fileid[:4])
for fileid in nltk.corpus.inaugural.fileids()
for w in nltk.corpus.inaugural.words(fileid)
for target in ['america', 'citizen']
if w.lower().startswith(target))
cfd.plot()


The result of generating the above plot is given below:

## Conclusion

That wraps up this tutorial on natural language processing in Python. In the next tutorial, we will take a bit of a breather and have fun with generating word clouds.

Part 3 of Natural Language Processing in Python