Learn Before
Chain Rule of Probability for Word Sequences
Represent a sequence of words as either or . The joint probability of observing this exact sequence is denoted as or . By applying the chain rule of probability, this joint probability can be decomposed into a product of conditional probabilities: .
0
1
Contributors are:
Who are from:
Tags
Data Science
Related
Huge Language Models
Bigram Model
N-Gram Model
Sentence Generation from Unigram Model
Unknown Words and Problem of Sparsity
Historical Significance and Applications of N-gram Models
A statistical language model is built to predict the next word in a sentence based on the probability of it occurring after the preceding sequence of words. This model is trained exclusively on a massive corpus of texts written in the 19th century. When this model is prompted with the partial sentence, 'To save the file, the user clicked the...', which outcome is the most probable explanation for its behavior?
Curse of Dimensionality in Traditional Language Models
Analyzing Zero Probability in an N-gram Model
Evaluating N-gram Model Complexity
Chain Rule of Probability for Word Sequences