We define a new formalism, based on Sikkel’s parsing schemata for constituency parsers, that can be used to describe, analyze and compare dependency parsing algorithms. This abstraction allows us to establish clear relations between several existing projective dependency parsers and prove their correctness.

This paper presents a novel top-down headdriven parsing algorithm for data-driven projective dependency analysis. This algorithm handles global structures, such as clause and coordination, better than shift-reduce or other bottom-up algorithms. Experiments on the English Penn Treebank data and the Chinese CoNLL-06 data show that the proposed algorithm achieves comparable results with other data...

We present and implement a fourth-order projective dependency parsing algorithm that effectively utilizes both “grand-sibling” style and “tri-sibling” style interactions of third-order and “grand-tri-sibling” style interactions of forth-order factored parts for performance enhancement. This algorithm requires O(n5) time and O(n4) space. We implement and evaluate the parser on two languages—Engl...

The paper introduces a dependency-based grammar and the associated parser and focusses on the problem of determinism in parsing and recovery from errors. First, it is shown how dependency-based parsing can be afforded, by taking into account the suggestions coming from other approaches, and the preference criteria for parsing are briefly addressed. Second, the issues of the interconnection betw...

This paper reports the results of experiments using memory-based learning to guide a deterministic dependency parser for unrestricted natural language text. Using data from a small treebank of Swedish, memory-based classifiers for predicting the next action of the parser are constructed. The accuracy of a classifier as such is evaluated on held-out data derived from the treebank, and its perfor...

Unsupervised dependency parsing aims to learn a dependency grammar from text annotated with only POS tags. Various features and inductive biases are often used to incorporate prior knowledge into learning. One useful type of prior information is that there exist correlations between the parameters of grammar rules involving different POS tags. Previous work employed manually designed features o...

Natural-Language Generation from flat semantics is an NP-complete problem. This makes it necessary to develop algorithms that run with reasonable efficiency in practice despite the high worstcase complexity. We show how to convert TAG generation problems into dependency parsing problems, which is useful because optimizations in recent dependency parsers based on constraint programming tackle ex...

Syntactic parsing is an important technique in the natural language processing, yet Latvian is still lacking an efficient general coverage syntax parser. This paper reports on the first experiments on statistical syntactic parsing for Latvian — a highly inflective Indo-European language with a relatively free word order. We have induced a statistical parser from a small, non-balanced Latvian Tr...

We describe in this paper an approach for synchronous parsing of syntactic and semantic dependency structures that combines recent advances in the area to get a very high accuracy as well at the same time very good parsing times. The time for parsing, the time for training and the values of the memory footprint are to our knowledge the best results reported while the parsing accuracy are as hig...

Less-configurational languages such as German often show not just morphological variation but also free word order and nonprojectivity. German is not exceptional in this regard, as other morphologically-rich languages such as Czech, Tamil or Greek, offer similar challenges that make context-free constituent parsing less attractive. Advocates of dependency parsing have long pointed out that the ...