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Language models in Python

Counting Bigrams: Version 1

The Natural Language Toolkit has data types and functions that make life easier for us when we want to count bigrams and compute their probabilities. The following code is best executed by copying it, piece by piece, into a Python shell.

import nltk

from nltk.corpus import brown

# an nltk.FreqDist() is like a dictionary,
# but it is ordered by frequency.
# Also, nltk automatically fills the dictionary
# with counts when given a list of words.

freq_brown = nltk.FreqDist(brown.words())

list(freq_brown.keys())[:20]
freq_brown.most_common(20)

# an nltk.ConditionalFreqDist() counts frequencies of pairs.
# When given a list of bigrams, it maps each first word of a bigram
# to a FreqDist over the second words of the bigram.

cfreq_brown_2gram = nltk.ConditionalFreqDist(nltk.bigrams(brown.words()))

# conditions() in a ConditionalFreqDist are like keys()
# in a dictionary

cfreq_brown_2gram.conditions()

# the cfreq_brown_2gram entry for "my" is a FreqDist.

cfreq_brown_2gram["my"]

# here are the words that can follow after "my".
# We first access the FreqDist associated with "my",
# then the keys in that FreqDist

cfreq_brown_2gram["my"].keys()

# here are the 20 most frequent words to come after "my", with their frequencies

cfreq_brown_2gram["my"].most_common(20)

# an nltk.ConditionalProbDist() maps pairs to probabilities.
# One way in which we can do this is by using Maximum Likelihood Estimation (MLE)

cprob_brown_2gram = nltk.ConditionalProbDist(cfreq_brown_2gram, nltk.MLEProbDist)

# This again has conditions() wihch are like dictionary keys

cprob_brown_2gram.conditions()

# Here is what we find for "my": a Maximum Likelihood Estimation-based probability distribution,
# as a MLEProbDist object.

cprob_brown_2gram["my"]

# We can find the words that can come after "my" by using the function samples()

cprob_brown_2gram["my"].samples()

# Here is the probability of a particular pair:



cprob_brown_2gram["my"].prob("own")

#####

# We can also compute unigram probabilities (probabilities of individual words)

freq_brown_1gram = nltk.FreqDist(brown.words())

len_brown = len(brown.words())


def unigram_prob(word):

return freq_brown_1gram[ word] / len_brown



#############

# The contents of cprob_brown_2gram, all these probabilities, now form a

# trained bigram language model. The typical use for a language model is

# to ask it for the probabillity of a word sequence

# P(how do you do) = P(how) * P(do|how) * P(you|do) * P(do | you)

prob_sentence = unigram_prob("how") * cprob_brown_2gram["how"].prob("do") * cprob_brown_2gram["do"].prob("you") * \

cprob_brown_2gram["you"].prob("do")

# result: 1.5639033871961e-09


###############

# We can also use a language model in another way:

# We can let it generate text at random

# This is not so useful, but can be insightful into what it is that

# the language model has been learning



cprob_brown_2gram["my"].generate()

# We can use this to generate text at random
# based on a given text of bigrams.
# Let's do this for the Sam "corpus"

corpus = """<s> I am Sam </s>
<s> Sam I am </s>
<s> I do not like green eggs and ham </s>"""

words = corpus.split()
cfreq_sam = nltk.ConditionalFreqDist(nltk.bigrams(words))
cprob_sam = nltk.ConditionalProbDist(cfreq_sam, nltk.MLEProbDist)

word = "<s>"
for index in range(50):
word = cprob_sam[ word].generate()
print(word, end = " ")

print("\n")

# Not a lot of variety. We need a bigger corpus.
# What kind of genres do we have in the Brown corpus?
brown.categories()

# Let's try Science Fiction.
cfreq_scifi = nltk.ConditionalFreqDist(nltk.bigrams(brown.words(categories = "science_fiction")))
cprob_scifi = nltk.ConditionalProbDist(cfreq_scifi, nltk.MLEProbDist)

word = "in"
for index in range(50):
word = cprob_scifi[ word ].generate()
print(word, end = " ")
print

# try this with other Brown corpus categories.


# Here is how to do this with NLTK books:
import nltk
from nltk.book import *

def generate_text(text, initialword, numwords):
bigrams = list(nltk.ngrams(text, 2))
cpd = nltk.ConditionalProbDist(nltk.ConditionalFreqDist(bigrams), nltk.MLEProbDist)


word = initialword
for i in range(numwords):
print(word, end = " ")
word = cpd[ word].generate()

print(word)

# Holy Grail
generate_text(text6, "I", 100)
# sense and sensibility
generate_text(text2, "I", 100)

Counting bigrams: Version 2

Please copy the following code into a text file. Call it from the command line, using the name of a file with text as an argument.

# Katrin Erk Oct 07
# Updated Feb 11
#
# Word bigrams are just pairs of words.
# In the sentence "I went to the beach"
# the bigrams are:
#    I went
#    went to
#    to the
#    the beach
#
# Having counts of English bigrams from a very large text corpus
# can be useful for a number of purposes.
#
# for example for spelling correction:
# If I had mistyped the sentence as "I went to beach"
# then I might be able to find the error by seeing that
# the bigram "to beach" has a very low count, and
# "to the", "to a", and "the beach" have much larger counts.
#
# This program counts all word bigrams in a given text file
#
# usage:
# python3 count_bigrams.py <filename>
#
# <filename> is a text file.

import string
import sys

# complain if we didn't get a filename
# as a command line argument
if len(sys.argv) < 2:
    print("Please enter the name of a corpus file as a command line argument.")
    sys.exit()
   
# try opening file
# If the file doesn't exist, catch the error
try:
    f = open(sys.argv[1])
except IOError:
    print("Sorry, I could not find the file", sys.argv[1])
    print("Please try again.")
    sys.exit()
   
# read the contents of the whole file into ''filecontents''
filecontents = f.read()
       
# count bigrams
bigrams = {}
words_punct = filecontents.split()
# strip all punctuation at the beginning and end of words, and
# convert all words to lowercase.
# The following is a Python list comprehension. It is a command that transforms a list,
# here words_punct, into another list.
words = [ w.strip(string.punctuation).lower() for w in words_punct ]

# add special START, END tokens
words = ["START"] + words + ["END"]

for index, word in enumerate(words):
    if index < len(words) - 1:
        # we only look at indices up to the
        # next-to-last word, as this is
        # the last one at which a bigram starts
        w1 = words[index]
        w2 = words[index + 1]
        # bigram is a tuple,
        # like a list, but fixed.
        # Tuples can be keys in a dictionary
        bigram = (w1, w2)

        if bigram in bigrams:
            bigrams[ bigram ] = bigrams[ bigram ] + 1
        else:
            bigrams[ bigram ] = 1
        # or, more simply, like this:
        # bigrams[bigram] = bigrams.get(bigram, 0) + 1

# sort bigrams by their counts
sorted_bigrams = sorted(bigrams.items(), key = lambda pair:pair[1], reverse = True)

for bigram, count in sorted_bigrams
    print(bigram, ":", count)


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