Implementing Voting Constraints With Finite State Transducers

We describe a constraint-based morphological disambiguation system in which individual constraint rules vote on matching morphological parses followed by its implementation using finite state transducers. Voting constraint rules have a number of desirable properties: The outcome of the disambiguation is independent of the order of application of the local contextual constraint rules. Thus the rule developer is relieved from worrying about conflicting rule sequencing. The approach can also combine statistically and manually obtained constraints, and incorporate negative constraints that rule out certain patterns. The transducer implementation has a number of desirable properties compared to other finite state tagging and light parsing approaches, implemented with automata intersection. The most important of these is that since constraints do not remove parses there is no risk of an overzealous constraint "killing a sentence ~ by removing all parses of a token during intersection. After a description of our approach we present preliminary results from tagging the Wall Street Journal Corpus with this approach. With about 400 statistically derived constraints and about 570 manual constraints, we can attain an accuracy of 97.82% on the training corpus and 97.29% on the test corpus. We then describe a finite state implementation of our approach and discuss various related issues.