Dependencias Universales para los treebanks AnCora

The present article describes the conversion of the Catalan and Spanish AnCora treebanks to the Universal Dependencies formalism. We describe the conversion process and assess the quality of the resulting treebank in terms of parsing accuracy by means of monolingual, cross-lingual and cross-domain parsing evaluation. The converted treebanks show an internal consistency comparable to the one shown by the original CoNLL09 distribution of AnCora, and indicate some differences in terms of multiword expression inventory with regards to the already existing UD Spanish treebank. The two new converted treebanks will be released in version 1.3 of Universal Dependencies.Este artículo presenta la conversión de los treebanks AnCora del catalán y el castellano al formalismo de Dependencias Universales (UD). Describimos el proceso de conversión y estimamos la calidad de los treebanks resultantes en términos de sus resultados en análisis sintáctico automático en un esquema monolingüe, en un esquema trans-lingüístico y en un tercero trans-dominio. Los treebanks convertidos muestran un nivel de consistencia interna de anotación comparable a la de los datos originales de la distribución CoNLL09 de AnCora, e indican algunas diferencias en términos del inventario de expresiones polilexemáticas con respecto al anterior treebank del castellano en UD. Los dos nuevos treebanks convertidos serán distribuidos con la versión 1.3 de Dependencias Universales

Converting Italian Treebanks: Towards an Italian Stanford Dependency Treebank

The paper addresses the challenge of converting MIDT, an existing dependencybased Italian treebank resulting from the harmonization and merging of smaller resources, into the Stanford Dependencies annotation formalism, with the final aim of constructing a standard–compliant resource for the Italian language. Achieved results include a methodology for converting treebank annotations belonging to the same dependency–based family, the Italian Stanford Dependency Treebank (ISDT), and an Italian localization of the Stanford Dependency scheme

Projective dependency parsing with perceptron

We describe an online learning dependency parser for the CoNLL-X Shared Task, based on the bottom-up projective algorithm of Eisner (2000). We experiment with a large feature set that models: the tokens involved in dependencies and their immediate context, the surfacetext distance between tokens, and the syntactic context dominated by each dependency. In experiments, the treatment of multilingual information was totally blind.Peer ReviewedPostprint (author’s final draft

Harmonization and Merging of two Italian Dependency Treebanks

The paper describes the methodology which is currently being defined for the construction of a "Merged Italian Dependency Treebank'' (MIDT) starting from already existing resources. In particular, it reports the results of a case study carried out on two available dependency treebanks, i.e. TUT and ISST--TANL. The issues raised during the comparison of the annotation schemes underlying the two treebanks are discussed and investigated with a particular emphasis on the definition of a set of linguistic categories to be used as a "bridge'' between the specific schemes. As an encoding format, the CoNLL de facto standard is used

Statistical parsing of morphologically rich languages (SPMRL): what, how and whither

The term Morphologically Rich Languages (MRLs) refers to languages in which significant information concerning syntactic units and relations is expressed at word-level. There is ample evidence that the application of readily available statistical parsing models to such languages is susceptible to serious performance degradation. The first workshop on statistical parsing of MRLs hosts a variety of contributions which show that despite language-specific idiosyncrasies, the problems associated with parsing MRLs cut across languages and parsing frameworks. In this paper we review the current state-of-affairs with respect to parsing MRLs and point out central challenges. We synthesize the contributions of researchers working on parsing Arabic, Basque, French, German, Hebrew, Hindi and Korean to point out shared solutions across languages. The overarching analysis suggests itself as a source of directions for future investigations

Cross-language Projection of Dependency Trees for Tree-to-tree Machine Translation

Syntax-based machine translation (MT) is an attractive approach for introducing addi-tional linguistic knowledge in corpus-based MT. Previous studies have shown that tree-to-string and string-to-tree translation mod-els perform better than tree-to-tree translation models since tree-to-tree models require two high quality parsers on the source as well as the target language side. In practice, high quality parsers for both languages are difficult to obtain and thus limit the translation quality. In this paper, we explore a method to transfer parse trees from the language side which has a high quality parser to the side which has a low quality parser to obtain transferred parse trees. We then combine the transferred parse trees with the original low quality parse trees. In our tree-to-tree MT experiments we have ob-served that the new combined trees lead to bet-ter performance in terms of BLEU score com-pared to when the original low quality trees and the transferred trees are used separately.