During training, we treat as observed the language word types W as well as the token-level corpus w. We utilize Gibbs sampling to approximate our collapsed model posterior: Note that given tag assignments T , there is only one setting of token-level tags t which has mass in the above posterior.
for the ith word type, the set of token-level tags associated with token occurrences of this word, denoted t (i) , must all take the value T i to have non-zero mass.