a value for n in an n-gram model, it is necessary to find the right trade-off between the stability of the estimate against its appropriateness. This means that trigram (i.e. triplets of words) is a common choice with large training corpora (millions of words), whereas a bigram is often used with smaller ones. Smoothing techniques There are problems of balance weight between infrequent grams (for example, if a proper name appeared in the training data) and frequent grams. Also, items not seen in the training data will be given a probability of 0.0 without smoothing. For unseen but plausible data from a sample, one can introduce pseudocounts. Pseudocounts are generally motivated on Bayesian grounds. In practice it is necessary to smooth the probability distributions by also assigning non-zero probabilities to unseen words or n-grams. The reason is that models derived directly from the n-gram frequency counts have severe problems when confronted with any n-grams that have not explicitly been seen before – the zero-frequency problem. Various smoothing methods are used, from simple "add-one" (Laplace) smoothing (assign a count of 1 to unseen n-grams; see Rule of succession) to more sophisticated models, such as Good–Turing discounting or back-off models. Some of these methods are equivalent to assigning a prior distribution to the probabilities of the n-grams and using Bayesian inference to compute the resulting posterior n-gram probabilities. However, the more sophisticated smoothing models were typically not derived in this fashion, but instead through independent considerations. Linear interpolation (e.g., taking the weighted mean of the unigram, bigram, and trigram) Good–Turing discounting Witten–Bell discounting Lidstone's smoothing Katz's back-off model (trigram) Kneser–Ney smoothing Skip-gram In the field of computational linguistics, in particular language modeling, skip-grams''' are a generalization of n-grams in which the components (typically words) need not be consecutive in the text under consideration, but may leave gaps that are skipped over. They provide one way of overcoming the data sparsity problem found with conventional n-gram analysis. In the area of computer security, skip-grams have proven more robust to attack than ngrams. Formally, an -gram is a consecutive subsequence of length of some sequence of tokens . A -skip--gram is a length- subsequence where the components occur at distance at most from each other. For example, in the input text:the rain in Spain falls mainly on the plainthe set of 1-skip-2-grams includes all the bigrams (2-grams), and in addition the subsequencesthe in, rain