User:Laurier Rochon/prototyping/npl

NPL 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