Learn 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)
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)
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)
This work introduces a general phrase recognition system based on perceptrons, and a global online learning algorithm to train them together. The method applies to complex domains in which some structure has to be recognized. This global problem is broken down into two layers of local subproblems: a filtering layer, which reduces the search space by(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 describes an experimental comparison between two standard supervised learning methods, namely Naive Bayes and Exemplar–based classification, on the Word Sense Disam-biguation (WSD) problem. The aim of the work is twofold. Firstly, it attempts to contribute to clarify some confusing information about the comparison between both methods appearing(More)
In this paper we present a very simple and effective part{of{speech tagger based on Support Vector Machines (SVM). Simplicity and ee-ciency are achieved by working with linear sep-arators in the primal formulation of SVM, and by using a greedy left-to-right tagging scheme. By means of a rigorous experimental evaluation, we conclude that the proposed(More)