$$P_{AbsoluteDiscounting}(w_i|w_{i-1}) = \frac{C(w_{i-1}w_i)-d}{\sum_vC(w_{i-1}v)}+\lambda(w_{i-1})P(w_i)$$
Where the first term is the discounted bigram, and the second term is the unigram with an interpolation weight $$\lambda$$.

University of California, Santa Cruz

Google

Absolute discounting is a method of discounting the counts for n-grams. In absolute discounting, a fixed (absolute) discount d is subtracted from each count. The intuition is that a small discount d will not significantly affect high count estimates, which are already well-estimated, but will modify the smaller counts in a generally positive way. 

Absolute Discounting

An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

Speech and Language Processing (3rd ed. draft) 

Kneser-Ney discounting augments absolute discounting to better handle low-order unigram distributions. Rather than estimating the probability of a word w simply appearing, we create a unigram model that estimates the probability of w appearing as a novel continuation, completing some previously unseen bigram. This estimate is based on the number of bigrams w has previously completed, with the intuition that words appearing in many different contexts will continue to do so, as opposed to those with more situational usage.

Learn Before

Related