Data-driven (statistical) approaches have been playing an increasingly prominent role in parsing since the 1990s. In recent years, there has been a growing interest in dependency-based as opposed to constituency-based approaches to syntactic parsing, with application to a wide range of research areas and different languages. Graph-based and transition-based methods are the two dominant data-dri...

This article relates results in data-driven dependency parsing of Swedish to linguistic generalizations regarding syntactic argument status, such as tendencies regarding animacy and definiteness, as well as properties more specific to the Scandinavian languages, such as finiteness. We show how datadriven modeling in combination with labeled dependency representations enable the acquisition of f...

In this paper, we describe and compare two statistical parsing approaches for the hybrid dependency-constituency syntactic representation used in the Quranic Arabic Treebank (Dukes and Buckwalter, 2010). In our first approach, we apply a multi-step process in which we use a shift-reduce algorithm trained on a pure dependency preprocessed version of the treebank. After parsing, the dependency ou...

We define broad-coverage semantic dependency parsing (SDP) as the task of recovering sentence-internal predicate–argument relationships for all content words, i.e. the semantic structure constituting the relational core of sentence meaning. 1 Background and Motivation Syntactic dependency parsing has seen great advances in the past decade, in part owing to relatively broad consensus on target r...

In dependency parsing, much effort is devoted to the development of new methods of language modeling and better feature settings. Less attention is paid to actual linguistic data and how appropriate they are for automatic parsing: linguistic data can be too complex for a given parser, morphological tags may not reflect well syntactic properties of words, a detailed, complex annotation scheme ma...

This paper presents a generative probabilistic dependency model of parallel texts that can be used for statistical machine translation and parallel parsing. Unlike syntactic models that are based on context-free dependency grammars, the dependency model proposed in this paper is based on a sophisticated notion of dependency grammar that is capable of modelling non-projective word order and isla...

In this paper, we investigate various strategies to predict both syntactic dependency parsing and contiguous multiword expression (MWE) recognition, testing them on the dependency version of French Treebank (Abeillé and Barrier, 2004), as instantiated in the SPMRL Shared Task (Seddah et al., 2013). Our work focuses on using an alternative representation of syntactically regular MWEs, which capt...

This thesis presents a computational model of working memory load in human sentence processing that attempts to explain a number of well-established linguistic performance effects that are associated with processing difficulty of certain syntactic structures. The computational model presented here is based on recent assumptions of working memory in computational cognitive modeling. In particula...

If all we want from a syntactic parser is a dependency tree, what do we gain by first computing a different representation such as a phrase structure tree? The principle of parsimony suggests that a simpler model should be preferred over a more complex model, all other things being equal, and the simplest model is arguably one that maps a sentence directly to a dependency tree – a bare-bones de...

If all we want from a syntactic parser is a dependency tree, what do we gain by first computing a different representation such as a phrase structure tree? The principle of parsimony suggests that a simpler model should be preferred over a more complex model, all other things being equal, and the simplest model is arguably one that maps a sentence directly to a dependency tree – a bare-bones de...