We show for both English POS tagging and Chinese word segmentation that with proper representation, large number of deterministic constraints can be learned from training examples, and these are useful in constraining probabilistic inference. For tagging, learned constraints are directly used to constrain Viterbi decoding. For segmentation, character-based tagging constraints can be learned with the same templates. However, they are better applied to a word-based model, thus an integer linear programming (ILP) formulation is proposed. For both problems, the corresponding constrained solutions have advantages in both efficiency and accuracy.
REF used it in Chinese word segmentation.
Exploring Deterministic Constraints: from a Constrained English POS Tagger to an Efficient ILP Solution to Chinese Word Segmentation
ACL
Computer Science