Elimination of Spurious Ambiguity in Transition-Based Dependency Parsing

We present a novel technique to remove spurious ambiguity from transition systems for dependency parsing. Our technique chooses a canonical sequence of transition operations (computation) for a given dependency tree. Our technique can be applied to a large class of bottom-up transition systems, including for instance Nivre (2004) and Attardi (2006)

Domain Adaptation for Dependency Parsing at Evalita 2011

The domain adaptation task was aimed at investigating techniques for adapting state-of-the-art dependency parsing systems to new domains. Both the language dealt with, i.e. Italian, and the target domain, namely the legal domain, represent two main novelties of the task organised at Evalita 2011. In this paper, we define the task and describe how the datasets were created from different resources. In addition, we characterize the different approaches of the participating systems, report the test results, and provide a first analysis of these results

Multilingual Dependency Parsing and Domain Adaptation using DeSR

We describe our experiments using the DeSR parser in the multilingual and domain adaptation tracks of the CoNLL 2007 shared task. DeSR implements an incremental deterministic Shift/Reduce parsing algorithm, using specific rules to handle non-projective dependencies. For the multilingual track we adopted a second order averaged perceptron and performed feature selection to tune a feature model for each language. For the domain adaptation track we applied a tree revision ethod which learns how to correct the mistakes made by the base parser on the adaptation domain

Deterministic choices in a data-driven parser.

Data-driven parsers rely on recommendations from parse models, which are generated from a set of training data using a machine learning classifier, to perform parse operations. However, in some cases a parse model cannot recommend a parse action to a parser unless it learns from the training data what parse action(s) to take in every possible situation. Therefore, it will be hard for a parser to make an informed decision as to what parse operation to perform when a parse model recommends no/several parse actions to a parser. Here we examine the effect of various deterministic choices on a datadriven parser when it is presented with no/several recommendation from a parse model

Adapting the TANL tool suite to Universal Dependencies

TANL is a suite of tools for text analytics based on the software architecture paradigm of data driven pipelines. The strategies for upgrading TANL to the use of Universal Dependencies range from a minimalistic approach consisting of introducing pre/post-processing steps into the native pipeline to revising the whole pipeline. We explore the issue in the context of the Italian Treebank, considering both the efforts involved, how to avoid losing linguistically relevant information and the loss of accuracy in the process

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

Evaluación de analizadores de constituyentes y de dependencias

El presente trabajo muestra la evaluación cuantitativa y cualitativa de un grupo de analizadores de constituyentes y de dependencias con el objetivo de ser usados en el desarrollo de una métrica automática basada en conocimiento para evaluar la salida de sistemas de traducción automática. Primero se describe la metodología seguida en ambos tipos de evaluación y a continuación se muestran los resultados obtenidos y las conclusiones alcanzadas.This work presents the quantitative and qualitative evaluation of a set of both constituency and dependency parsers which are to be used in the development of a knowledge-based automatic MT metric. Firstly, the methodology used in both types of evaluation is described; secondly, we show the results obtained, and finally we draw some conclusions.This work has been funded by the Spanish Government project KNOW, TIN2009-14715-C0403

Constituency and Dependency Parsers Evaluation

This work presents the quantitative and qualitative evaluation of a set of both constituency and dependency parsers which are to be used in the development of a knowledgebased automatic MT metric. Firstly, the methodology used in both types of evaluation is described; secondly, we show the results obtained, and finally we draw some conclusions