Since these long words are often predictable from some character level information including suffixes or character types, in a Type Feature Character type at time t−i (0 ≤ i ≤ 4) cont # of the same character types before t ch # of times character types changed within 8 characters before t semi-supervised setting we employ a Negative Binomial generalized linear model (GLM) for setting L t adaptively for each character position t in the corpus.
we model the word length ℓ by a Negative Binomial distribution (Cook, 2009): This counts the number of failures of Bernoulli draws with probability (1−p) before r'th success.