What is absolute discounting?
The basic idea is to subtract a constant from all counts r>0 and thus, in particular, to leave the high counts virtually intact.
What is absolute discounting and its advantage?
One of the advantages of absolute discounting is that it gradually transitions between values that are close to the empirical distribution for abundant categories (since µ then dominates the discount δ), to a behavior that imitates the Good–Turing estimator for rare categories (as established by Lemma 5).
What is Witten Bell smoothing?
Witten-Bell smoothing is this smoothing algorithm that was invented by some dude named Moffat, but dudes named Witten and Bell have generally gotten credit for it. It is significant in the field of text compression and is relatively easy to implement, and that’s good enough for us.
Why is smoothing so important for n-gram modeling?
In practice it is necessary to smooth the probability distributions by also assigning non-zero probabilities to unseen words or n-grams.
When to use smoothing to avoid 0 probability?
if the prior prefers models that don’t assign 0 probability to any w, then at the end we won’t have 0 entries adjusting MLE to avoid 0 probability is called “smoothing” – it’s a form of regularization.
How is Kneser Ney different from absolute discounting?
Whereas absolute discounting interpolation in a bigram model would simply default to a unigram model in the second term, Kneser-Ney depends upon the idea of a continuation probability associated with each unigram. This probability for a given token \\(w_i\\) is proportional to the number of bigrams which it completes:
When to use Laplace smoothing or Additive smoothing?
Laplace Smoothing (or Additive Smoothing): it may not be what we want: maybe we want to give more or less weight to certain words if we have a corpus, then we can use this corpus to learn the LM on the entire corpus There are two ways of building the Collection LM:
When to use smoothing in a retrieval function?
Gale, William, and Geoffrey Sampson. “Good-Turing smoothing without tears.” 1995. [1] Hiemstra, Djoerd. “A probabilistic justification for using tf× idf term weighting in information retrieval.” 2000. [2] Let’s derive a query retrieval function using the smoothed log likelihood: