This paper discusses the performance difference of wide-coverage parsers on small-domain speech transcripts. Two parsers (C&C CCG and RASP) are tested on the speech transcripts of two different domains (parent-child language, and picture descriptions). The performance difference between the domain-independent parsers and two domain-trained parsers (MSTParser and MEGRASP) is substantial, with a ...

We introduce interpolation of trained MSTParser models as a resource combination method for multi-source delexicalized parser transfer. We present both an unweighted method, as well as a variant in which each source model is weighted by the similarity of the source language to the target language. Evaluation on the HamleDT treebank collection shows that the weighted model interpolation performs...

A method is presented for transferring dependency treebanks between similar languages by using a bilingual lexicon, aiming to improve dependency parsing accuracy on the target language. It is illustrated by transferring the Slovene Dependency Treebank to Croatian by using a GIZA++ bilingual lexicon constructed from the Croatian-Slovene 1984 parallel corpus from the Multext East project. The tra...

In this paper we present a novel phrase structure parsing approach with the help of dependency structure. Different with existing phrase parsers, in our approach the inference procedure is guided by dependency structure, which makes the parsing procedure flexibly. The experimental results show our approach is much more accurate. With the help of golden dependency trees, F1 score of our parser a...

The paper describes a data driven dependency parsing approach which uses clausal information of a sentence to improve the parser performance. The clausal information is added automatically during the parsing process. We demonstrate the experiments on Hindi, a language with relatively rich case marking system and free-word-order. All the experiments are done using a modified version of MSTParser...

This paper proposes a combined model for POS tagging, dependency parsing and co-reference resolution for Bulgarian — a pro-drop Slavic language with rich morphosyntax. We formulate an extension of the MSTParser algorithm that allows the simultaneous handling of the three tasks in a way that makes it possible for each task to benefit from the information available to the others, and conduct a se...

In this paper we explore two strategies to incorporate local morphosyntactic features in Hindi dependency parsing. These features are obtained using a shallow parser. We first explore which information provided by the shallow parser is most beneficial and show that local morphosyntactic features in the form of chunk type, head/non-head information, chunk boundary information, distance to the en...

We present two experiments with ensemble parsing, in which we obtain a 1.4% improvement of UAS compared to the best parser. We use five parsers: MateParser, TurboParser, Parsito, MaltParser a MSTParser, and the data of the analytical layer of Prague Dependency Treebank (1.5 million tokens). We split training data into 10 data-splits and run a 10-fold cross-validation scheme with each of the fiv...

In this paper, we attempt to solve the problem of Prepositional Phrase (PP) attachments in English. The motivation for the work comes from NLP applications like Machine Translation, for which, getting the correct attachment of prepositions is very crucial. The idea is to correct the PPattachments for a sentence with the help of alignments from parallel data in another language. The novelty of o...

Dependency parsers are usually evaluated on attachment accuracy. Whilst easily interpreted, the metric does not illustrate the cascading impact of errors, where the parser chooses an incorrect arc, and is subsequently forced to choose further incorrect arcs elsewhere in the parse. We apply arc-level constraints to MSTparser and ZPar, enforcing the correct analysis of specific error classes, whi...