Learn Before
Huge Language Models
Efficiency considerations are important when building language models that use such large sets of n-grams. Some techniques are:
- Storing words as 64-bit hash numbers in memory as opposed to strings.
- Pruning, only storing n-grams with counts greater than some threshold.
- Building approximate language models.
- Stupid backoff
0
3
Tags
Data Science
Related
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
Learn After
Learn After
Related
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