Semantic dependency parsing aims at extracting arcs and semantic role labels for all words in a sentence. In this paper, we propose a semantic dependency parser which is based on Long Short-term Memory and makes heavy use of embeddings of words and POS tags. We describe in detail the implementation of the neural parser, including preprocessing, postprocessing and various input features, and sho...

This paper describes a wide-coverage statistical parser that uses Combinatory Categorial Grammar (CCG) to derive dependency structures. The parser differs from most existing wide-coverage treebank parsers in capturing the long-range dependencies inherent in constructions such as coordination, extraction, raising and control, as well as the standard local predicate-argument dependencies. A set o...

We present a conversion from the dependency scheme employed by the Pro3Gres parser to the Stanford scheme, as a further step towards unification of dependency schemes. An evaluation of the conversion shows that it is highly reliable, resulting in less than one percentage point performance penalty on the actual parser output. This supports the suitability of the Stanford scheme as a unifying rep...

This paper describes a wide-coverage statistical parser that uses Combinatory Categorial Grammar (CCG) to derive dependency structures. The parser differs from most existing wide-coverage treebank parsers in capturing the long-range dependencies inherent in constructions such as coordination, extraction, raising and control, as well as the standard local predicate-argument dependencies. A set o...

In this paper, we describe a two-stage multilingual dependency parser used for the multilingual track of the CoNLL 2007 shared task. The system consists of two components: an unlabeled dependency parser using Gibbs sampling which can incorporate sentence-level (global) features as well as token-level (local) features, and a dependency relation labeling module based on Support Vector Machines. E...

The present paper describes a three stage technique to parse Hindi sentences. In the first stage we create a model with the features of head words of each chunk and their dependency relations. Here, the dependency relations are inter-chunk dependency relations. We have experimentally fixed a feature set for learning this model. In the second stage, we extract the intra-chunk dependency relation...

We present a data-driven parser that derives both constituent structures and dependency structures, alone or in combination, in one and the same process. When trained and tested on data from the Swedish treebank Talbanken05, the parser achieves a labeled dependency accuracy of 82% and a labeled bracketing F-score of 75%.

We present a dependency parser for Persian, called ParsPer, developed using the graph-based parser in the Mate Tools. The parser is trained on the entire Uppsala Persian Dependency Treebank with a specific configuration that was selected by MaltParser as the best performing parsing representation. The treebank’s syntactic annotation scheme is based on Stanford Typed Dependencies with extensions...

This paper presents a thorough examination of the validity of three evaluation measures on parser output. We assess parser performance of an unlexicalised probabilistic parser trained on two German treebanks with different annotation schemes and evaluate parsing results using the PARSEVAL metric, the Leaf-Ancestor metric and a dependency-based evaluation. We reject the claim that the TüBa-D/Z a...

Due to the data sparseness problem, the lexical information from a treebank for a lexicalized parser could be insufficient. This paper proposes an approach to learn head-modifier pairs from a raw corpus, and to integrate them into a lexicalized dependency parser to parse a Chinese Treebank. Experimental results show that this approach not only enlarged the coverage of bi-lexical dependency, but...