emnlp emnlp2010 emnlp2010-116 emnlp2010-116-reference knowledge-graph by maker-knowledge-mining
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Author: Tahira Naseem ; Harr Chen ; Regina Barzilay ; Mark Johnson
Abstract: We present an approach to grammar induction that utilizes syntactic universals to improve dependency parsing across a range of languages. Our method uses a single set of manually-specified language-independent rules that identify syntactic dependencies between pairs of syntactic categories that commonly occur across languages. During inference of the probabilistic model, we use posterior expectation constraints to require that a minimum proportion of the dependencies we infer be instances of these rules. We also automatically refine the syntactic categories given in our coarsely tagged input. Across six languages our approach outperforms state-of-theart unsupervised methods by a significant margin.1
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