The probability distribution on a factor graph with templates Θ becomes The task of finding meaningful skip chains corresponds to finding a set of templatesΘ describing A linear chain template θ lc with features g lc ( x, j) for all possible combinations of label variables is assumed to be present in all configurations.
in the following, the set of templates to select from is defined by properties a skip chain factor is added to connect two labeling variables of two tokens if a specified property holds (where we take every occurring feature specified for the linear chain into account, like bag-of-words, prefixes, suffixes of tokens as well as several regular expressions, the full set is given by ).