Lluís Màrquez i Villodre

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The Conference on Computational Natural Language Learning is accompanied every year by a shared task whose purpose is to promote natural language processing applications and evaluate them in a standard setting. In 2008 the shared task was dedicated to the joint parsing of syntactic and semantic dependencies. This shared task not only unifies the shared(More)
This paper presents the SVMTool, a simple, flexible, effective and efficient part–of–speech tagger based on Support Vector Machines. The SVMTool offers a fairly good balance among these properties which make it really practical for current NLP applications. It is very easy to use and easily configurable so as to perfectly fit the needs of a number of(More)
For the 11th straight year, the Conference on Computational Natural Language Learning has been accompanied by a shared task whose purpose is to promote natural language processing applications and evaluate them in a standard setting. In 2009, the shared task was dedicated to the joint parsing of syntactic and semantic dependencies in multiple languages.(More)
Semantic role labeling, the computational identification and labeling of arguments in text, has become a leading task in computational linguistics today. Although the issues for this task have been studied for decades, the availability of large resources and the development of statistical machine learning methods have heightened the amount of effort in this(More)
This paper describes a set of comparative experiments for the problem of automatically filtering unwanted electronic mail messages. Several variants of the AdaBoost algorithm with confidence– rated predictions (Schapire & Singer 99) have been applied, which differ in the complexity of the base learners considered. Two main conclusions can be drawn from our(More)
This paper presents the task ‘Coreference Resolution in Multiple Languages’ to be run in SemEval-2010 (5th International Workshop on Semantic Evaluations). This task aims to evaluate and compare automatic coreference resolution systems for three different languages (Catalan, English, and Spanish) by means of two alternative evaluation metrics, thus(More)
This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and(More)
Evaluation results recently reported by Callison-Burch et al. (2006) and Koehn and Monz (2006), revealed that, in certain cases, the BLEU metric may not be a reliable MT quality indicator. This happens, for instance, when the systems under evaluation are based on different paradigms, and therefore, do not share the same lexicon. The reason is that, while MT(More)
In this paper we present a semantic role labeling system submitted to the CoNLL2005 shared task. The system makes use of partial and full syntactic information and converts the task into a sequential BIO-tagging. As a result, the labeling architecture is very simple . Building on a state-of-the-art set of features, a binary classifier for each label is(More)