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Brute-force word sense disambiguation (WSD) algorithms based on semantic relatedness are really time consuming. We study how to perform WSD faster and better on the span of a text. Several stochastic algorithms can be used to perform Global WSD. We focus here on an Ant Colony Algorithm and compare it to two other methods (Genetic and Simulated Annealing(More)
OMNIA is an ongoing project that aims to retrieve images accompanied with multilingual texts. In this paper, we propose a generic method (language and domain independent) to extract conceptual information from such texts and spontaneous user requests. First, texts are labelled with interlingual annotation, then a generic extractor taking a domain on-tology(More)
This article presents the GETALP system for the participation to SemEval-2013 Task 12, based on an adaptation of the Lesk measure propagated through an Ant Colony Algorithm, that yielded good results on the corpus of Se-meval 2007 Task 7 (WordNet 2.1) as well as the trial data for Task 12 SemEval 2013 (Ba-belNet 1.0). We approach the parameter estimation to(More)
Word Sense Disambiguation (WSD) is a difficult problem for NLP. Algorithm that aim to solve the problem focus on the quality of the disambiguation alone and require considerable computational time. In this article we focus on the study of three unsupervised stochastic algorithms for WSD: a Genetic Algorithm (GA) and a Simulated Annealing algorithm (SA) from(More)
Conceptual vectors can be used to represent thematic aspects of text segments, which allow for the computation of semantic relatedness. We study the behavior of conceptual vectors based on an ontology by comparing the results to the Miller-Charles benchmark. We discuss the limits to such an approach due to explicit mapping, as well as the viability of the(More)