Parsing as Reduction

We reduce phrase-representation parsing to dependency parsing. Our reduction is grounded on a new intermediate representation, "head-ordered dependency trees", shown to be isomorphic to constituent trees. By encoding order information in the dependency labels, we show that any off-the-shelf, trainable dependency parser can be used to produce constituents. When this parser is non-projective, we can perform discontinuous parsing in a very natural manner. Despite the simplicity of our approach, experiments show that the resulting parsers are on par with strong baselines, such as the Berkeley parser for English and the best single system in the SPMRL-2014 shared task. Results are particularly striking for discontinuous parsing of German, where we surpass the current state of the art by a wide margin

Efficient Parsing with Linear Context-Free Rewriting Systems

Previous work on treebank parsing with discontinuous constituents using Linear Context-Free Rewriting systems (LCFRS) has been limited to sentences of up to 30 words, for reasons of computational complexity. There have been some results on binarizing an LCFRS in a manner that minimizes parsing complexity, but the present work shows that parsing long sentences with such an optimally binarized grammar remains infeasible. Instead, we introduce a technique which removes this length restriction, while maintaining a respectable accuracy. The resulting parser has been applied to a discontinuous treebank with favorable results.