Towards a Weighted Induction Method of Dependency Annotation

Abstract

This paper presents a method of annotating sentences with dependency trees which is set within the mainstream of the study on dependency projection. The approach builds on the idea of weighted projection. However, we involve a weighting factor not only in the process of projecting dependency relations (weighted projection) but also in the process of acquiring dependency trees from projected sets of dependency relations (weighted induction). Using a parallel corpus, its source side is automatically annotated with a syntactic parser and resulting dependencies are transferred to equivalent target sentences via an extended set of word alignment links. Projected relations are initially weighted according to the certainty of word alignment links used in projection. Since word alignments may be noisy and we should not entirely rely on them, initial weights are thus recalculated using a version of the EM algorithm. Then, maximum spanning trees fulfilling properties of well-formed dependency structures are selected from EM-scored directed graphs. An extrinsic evaluation shows that parsers trained on induced trees perform comparably to parsers trained on a manually developed treebank.