This paper proposes a discriminative forest reranking algorithm for dependency parsing that can be seen as a form of efficient stacked parsing. A dynamic programming shift-reduce parser produces a packed derivation forest which is then scored by a discriminative reranker, using the 1-best tree output by the shift-reduce parser as guide features in addition to third-order graph-based features. T...

In recent years, research in parsing has extended in several new directions. One of these directions is concerned with parsing languages other than English. Treebanks have become available for many European languages, but also for Arabic, Chinese, or Japanese. However, it was shown that parsing results on these treebanks depend on the types of treebank annotations used [ , ]. Another direction ...

The goal of this article is to present our work about a combination of several syntactic parsers to produce a more robust parser. We have built a platform which allows us to compare syntactic parsers for a given language by splitting their results in elementary pieces, normalizing them, and comparing them with reference results. The same platform is used to combine several parsers to produce a ...

In this paper, we present a new syntactically annotated corpus consisting of daily notes from an intensive care unit in a Finnish hospital. Using the corpus, we perform experiments with both rule-based and statistical parsers. We apply an existing rule-based parser specifically developed for this clinical language and create a set of conversion rules for transforming the constituency scheme of ...

Transition-based dependency parsers usually use transition systems that monotonically extend partial parse states until they identify a complete parse tree. Honnibal et al. (2013) showed that greedy onebest parsing accuracy can be improved by adding additional non-monotonic transitions that permit the parser to “repair” earlier parsing mistakes by “over-writing” earlier parsing decisions. This ...

We connect two scenarios in structured learning: adapting a parser trained on one corpus to another annotation style, and projecting syntactic annotations from one language to another. We propose quasisynchronous grammar (QG) features for these structured learning tasks. That is, we score a aligned pair of source and target trees based on local features of the trees and the alignment. Our quasi...

We investigate the performance of an easyfirst, non-directional dependency parser on the Hebrew Dependency treebank. We show that with a basic feature set the greedy parser’s accuracy is on a par with that of a first-order globally optimized MST parser. The addition of morphological-agreement feature improves the parsing accuracy, making it on-par with a second-order globally optimized MST pars...

“Deep-syntactic” dependency structures that capture the argumentative, attributive and coordinative relations between full words of a sentence have a great potential for a number of NLPapplications. The abstraction degree of these structures is in-between the output of a syntactic dependency parser (connected trees defined over all words of a sentence and language-specific grammatical functions...

The paper discusses the transferring rules of the output from a dependency parser for Bulgarian into RMRS analyses. This task is required by the machine translation compatibility between Bulgarian and English resources. Since the Bulgarian HPSG grammar is still being developed, a repairing mechanism has been envisaged by parsing the Bulgarian data with the Malt Dependency Parser, and then retri...

We report a Japanese parsing system with a linguistically fine-grained grammar based on the Lexical-Functional Grammar (LFG) formalism. The system is the first Japanese LFG parser with over 97% coverage for real-world text. We evaluated the accuracy of the system comparing it with standard Japanese dependency parsers. The LFG parser shows roughly equivalent performance on the dependency accurac...