User:Laurier Rochon/prototyping/nlp

NLP with NLTK/Python

Count number of words (word tokens) : len(text)
Count number of distinct words (word types) : len(set(text))
The diversity of a text can be found with : len(text) / len(set(text))
Dispersion plot : shows you usage of certain words in time (useful for quick overviews) (i.e. text.dispersion_plot(['of','the']))
Collocations : 2 words that are almost always together (i.e. red wine) text.collocations()
Join/split to create strings/lists from delimiters
All the words starting with B in text 5. Sorted and unique words only : sorted([w for w in set(text5) if w.startswith('b')])
Find exact occurrence of a word = text.index('word')
Find all 4-letter words in a text :

V = set(text8)
fourletter = [w for w in V if len(w)==4]
sorted(fourletter)

And show their distribution in order

fdist = FreqDist(text5)
vocab = fdist.keys()
for w in vocab:
	if len(w)==4:
		print w

Find all words containing 'ma' in them, sorted.

res = sorted([w for w in set(text) if 'ma' in w])

How often a given word occurs in a text, expressed as a percentage

fdist = FreqDist(text)
fdist['word']/len(text)

Find occurences of a word, in context : text.concordance("term")

Gutenberg stuff

To access raw text : len(gutenberg.raw('blake-poems.txt'). This returns the letters, including spaces, instead of words. macbeth_sentences = gutenberg.sents('shakespeare-macbeth.txt') would split things up in sentences. We can also use the words() method to break things into words : emma = nltk.Text(nltk.corpus.gutenberg.words('austen-emma.txt'))

Find certain words in a text, and how many times they appear

from nltk.corpus import brown
import nltk

news_text = brown.words(categories="news")
fdist = nltk.FreqDist([w.lower() for w in news_text])
modals = ['what','where','who','why']
for m in modals:
    print m + " : ", fdist[m]

result :

what :  95
where :  59
who :  268
why :  14

Plot a graph to show the use of the words 'america' and 'citizen'

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

Load up the brown corpus, create a frequency distribution, and print out the 'modals' with the number of occurences.

from nltk.corpus import brown
import nltk
 
news_text = brown.words(categories="news")
fdist = nltk.FreqDist([w.lower() for w in news_text])
modals = ['what','where','who','why']
for m in modals:
    print m + " : ", fdist[m]

Creating pairings with different lists

from nltk.corpus import brown
import nltk

l1 = ["a word","another word","and another again"]
l2 = ["cat 1", "cat 2"]

somevalues = [
    (v1, v2)
    for v1 in l2
    for v2 in l1
]

print somevalues

will output

[('cat 1', 'a word'), ('cat 1', 'another word'), ('cat 1', 'and another again'), 
('cat 2', 'a word'), ('cat 2', 'another word'), ('cat 2', 'and another again')]