GPLSI-IXA: Using Semantic Classes to Acquire Monosemous Training Examples from Domain Texts
Rubén Izquierdo, Armando Suárez Cueto, Germán Rigau · 2010
This paper summarizes our participation in task #17 of SemEval–2 (All–words WSD on a specific domain) using a supervised class-based Word Sense Disambiguation system. Basically, we use Support Vector Machines (SVM) as learning algorithm and a set of simple features to build three different models. Each model considers a different training corpus: Sem-Cor (SC), examples from monosemous words extracted automatically from background data (BG), and both SC and BG (SCBG). Our system explodes the monosemous words appearing as members of a particular WordNet semantic class to automatically acquire class-based annotated examples from the domain text. We use the class-based examples gathered from the domain corpus to adapt our traditional system trained on SemCor. The evaluation reveal that the best results are achieved training with SemCor and the background examples from monosemous words, obtaining results above the first sense baseline and the fifth best position in the competition rank. 1