A Constraint Satisfaction Approach to Dependency Parsing

We present an adaptation of constraint satisfaction inference (Canisius et al., 2006b) for predicting dependency trees. Three different classifiers are trained to predict weighted soft-constraints on parts of the complex output. From these constraints, a standard weighted constraint satisfaction problem can be formed, the solution to which is a valid dependency tree.

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