Paper: Using Universal Linguistic Knowledge to Guide Grammar Induction

ACL ID D10-1120
Title Using Universal Linguistic Knowledge to Guide Grammar Induction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

We present an approach to grammar induc- tion that utilizes syntactic universals to im- prove dependency parsing across a range of languages. Our method uses a single set of manually-specified language-independent rules that identify syntactic dependencies be- tween pairs of syntactic categories that com- monly occur across languages. During infer- ence of the probabilistic model, we use pos- terior expectation constraints to require that a minimum proportion of the dependencies we infer be instances of these rules. We also auto- matically refine the syntactic categories given in our coarsely tagged input. Across six lan- guages our approach outperforms state-of-the- artunsupervisedmethodsbyasignificantmar- gin.1