Abstract
For many languages, we are not able to train any supervised parser, because there are no manually annotated data available. This problem can be solved by using a parallel corpus with English, parsing the English side, projecting the dependencies through word-alignment connections, and training a parser on the projected trees. In this paper, we introduce a simple algorithm using a combination of various word-alignment symmetrizations. We prove that our method outperforms previous work, even though it uses McDonald’s maximum-spanning-tree parser as it is, without any “unsupervised” modifications.
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Mareček, D. (2011). Combining Diverse Word-Alignment Symmetrizations Improves Dependency Tree Projection. In: Gelbukh, A.F. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19400-9_12
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DOI: https://doi.org/10.1007/978-3-642-19400-9_12
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