Language modeling
Language modeling is the task of predicting the probability of a sequence of words in a language. It is a fundamental problem in natural language processing and is used for many NLP tasks such as speech recognition, machine translation, and text generation. The basic idea behind language modeling is to estimate the probability distribution of a sequence of words, given a context of previous words.
A simple way to build a language model is to use n-grams, which are sequences of n words. The probability of a word given its previous n-1 words can be estimated using the n-gram frequency in a corpus of text. For example, to estimate the probability of the word "run" given the two previous words "I" and "will", we would count the number of times the sequence "I will run" occurs in the corpus and divide it by the number of times the sequence "I will" occurs.
Here is an example code for building a simple bigram language model using NLTK library:
import nltk
from nltk.corpus import brown
# get the brown corpus
corpus = brown.sents()
# build the bigram model
bigram_model = nltk.LM(order=2)
bigram_model.fit(corpus, vocabulary_text=brown.words())
# test the model
test_sentence = "I will run tomorrow"
words = test_sentence.split()
prob = 1.0
for i in range(1, len(words)):
prev_word = words[i-1]
curr_word = words[i]
prob *= bigram_model.score(curr_word, [prev_word])
print("Probability of sentence:", prob)
In this example, we first load the Brown corpus from NLTK and use it to build a bigram language model using the nltk.LM class. We then test the model by calculating the probability of the test sentence "I will run tomorrow" using the bigram probabilities estimated from the corpus. We compute the probability of each word given its previous word using the score method of the language model, and multiply them to obtain the probability of the whole sentence.
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