The "Meaning" system on the English all-words task

The “Meaning” system has been developed within the framework of the Meaning European research project1 . It is a combined system, which integrates several supervised machine learning word sense disambiguation modules, and several knowledge– based (unsupervised) modules. See section 2 for details. The supervised modules have been trained exclusively on the SemCor corpus, while the unsupervised modules use WordNet-based lexico–semantic resources integrated in the Multilingual Central Repository (MCR) of the Meaning project (Atserias et al., 2004). The architecture of the system is quite simple. Raw text is passed through a pipeline of linguistic processors (tokenizers, POS tagging, named entity extraction, and parsing) and then a Feature Extraction module codifies examples with features extracted from the linguistic annotation and MCR. The supervised modules have priority over the unsupervised and they are combined using a weighted voting scheme. For the words lacking training examples, the unsupervised modules are applied in a cascade sorted by decreasing precision. The tuning of the combination setting has been performed on the Senseval-2 allwords corpus. Several research groups have been providers of resources and tools, namely: IXA group from the University of the Basque Country, ITC-irst (“Istituto per la Ricerca Scientifica e Tecnologica”), University of Sussex (UoS), University of Alicante (UoA), and TALP research center at the Technical University of Catalonia. The integration was carried out by the TALP group.