Beginner Explanation
Imagine you’re playing a game where you have to guess the next word in a sentence. A statistical language model is like a super-smart friend who has read a lot of books and knows which words usually come next. For example, if you say ‘The cat sat on the’, your friend might guess ‘mat’ because that’s a common ending. It learns from patterns in language, just like how you learn from hearing people talk every day.Technical Explanation
Statistical language models use probabilities to predict the next word in a sequence based on the preceding words. One common type is the n-gram model, which considers the previous ‘n’ words to predict the next one. For example, in a bigram model (n=2), the probability of the next word is calculated as P(w_n | w_{n-1}). This can be implemented in Python using libraries like NLTK. Here’s a simple code snippet: “`python from nltk import bigrams, FreqDist from nltk.corpus import brown # Load text text = brown.words(categories=’news’) # Create bigrams bigrams_list = list(bigrams(text)) # Calculate frequency distribution bigram_freq = FreqDist(bigrams_list) # Get probability of a word given the previous word prob = bigram_freq[(‘the’, ‘cat’)] / bigram_freq[‘the’] print(prob) “`Academic Context
Statistical language models have their roots in probability theory and information theory. They are essential for tasks such as speech recognition, machine translation, and text generation. The n-gram model, one of the earliest statistical language models, was popularized by the work of Claude Shannon in the 1950s. Key papers include ‘A Mathematical Theory of Communication’ by Shannon and ‘Statistical Methods for Speech Recognition’ by Rabiner. The mathematical foundation involves Markov assumptions and the use of maximum likelihood estimation for parameter training.Code Examples
Example 1:
from nltk import bigrams, FreqDist
from nltk.corpus import brown
# Load text
text = brown.words(categories='news')
# Create bigrams
bigrams_list = list(bigrams(text))
# Calculate frequency distribution
bigram_freq = FreqDist(bigrams_list)
# Get probability of a word given the previous word
prob = bigram_freq[('the', 'cat')] / bigram_freq['the']
print(prob)Example 2:
from nltk import bigrams, FreqDist
from nltk.corpus import brown
# Load text
text = brown.words(categories='news')Example 3:
from nltk.corpus import brown
# Load text
text = brown.words(categories='news')
# Create bigramsView Source: https://arxiv.org/abs/2511.16577v1