We describe our CoNLL 2008 Shared Task system in this paper. The system includes two cascaded components: a syntactic and a semantic dependency parsers. A firstorder projective MSTParser is used as our syntactic dependency parser. In order to overcome the shortcoming of the MSTParser, that it cannot model more global information, we add a relabeling stage after the parsing to distinguish some c...

This paper investigates the impact of different morphological and lexical information on data-driven dependency parsing of Persian, a morphologically rich language. We explore two state-of-the-art parsers, namely MSTParser andMaltParser, on the recently released Persian dependency treebank and establish some baselines for dependency parsing performance. Three sets of issues are addressed in our...

This paper presents a new conversion method to automatically transform a constituent-based Vietnamese Treebank into dependency trees. On a dependency Treebank created according to our new approach, we examine two stateof-the-art dependency parsers: the MSTParser and the MaltParser. Experiments show that the MSTParser outperforms the MaltParser. To the best of our knowledge, we report the highes...

We present two dependency parsers for Persian, MaltParser and MSTParser, trained on the Uppsala PErsian Dependency Treebank. The treebank consists of 1,000 sentences today. Its annotation scheme is based on Stanford Typed Dependencies (STD) extended for Persian with regard to object marking and light verb contructions. The parsers and the treebank are developed simultanously in a bootstrapping ...

This paper proposes a dependency treebased SRL system with proper pruning and extensive feature engineering. Official evaluation on the CoNLL 2008 shared task shows that our system achieves 76.19 in labeled macro F1 for the overall task, 84.56 in labeled attachment score for syntactic dependencies, and 67.12 in labeled F1 for semantic dependencies on combined test set, using the standalone Malt...

Preliminary experiments presented in this paper consist in the induction and evaluation of a dependency parser for Polish. We train data-driven dependency models with publicly available parser-generation systems (MaltParser and MSTParser) given a converted dependency structure bank for Polish. Induced Polish dependency parsers are evaluated against a set of gold standard dependency structures u...

In this paper, we introduce our work on SemEval-2012 task 5: Chinese Semantic Dependency Parsing. Our system is based on MSTParser and two effective methods are proposed: splitting sentence by punctuations and extracting last character of word as lemma. The experiments show that, with a combination of the two proposed methods, our system can improve LAS about one percent and finally get the sec...

We combine multiple word representations based on semantic clusters extracted from the (Brown et al., 1992) algorithm and syntactic clusters obtained from the Berkeley parser (Petrov et al., 2006) in order to improve discriminative dependency parsing in the MSTParser framework (McDonald et al., 2005). We also provide an ensemble method for combining diverse cluster-based models. The two contrib...

We evaluate two dependency parsers, MSTParser and MaltParser, with respect to their capacity to recover unbounded dependencies in English, a type of evaluation that has been applied to grammarbased parsers and statistical phrase structure parsers but not to dependency parsers. The evaluation shows that when combined with simple post-processing heuristics, the parsers correctly recall unbounded ...

We propose a linguistically motivated set of features to capture morphological agreement and add them to the MSTParser dependency parser. Compared to the built-in morphological feature set, ours is both much smaller and more accurate across a sample of 20 morphologically annotated treebanks. We find increases in accuracy of up to 5.3% absolute. While some of this results from the feature set ca...