Fecha: 17 de Junio de 2013
Ponente: Eneko Agirre (Euskal Herriko Unibertsitatea/Universidad del País Vasco)
Lugar de celebración: Salón de Actos, Facultad de Psicología, UNED
Given a sentence, semantic roles indicate "who" did "what" to "whom", "when" and "where". Given a predicate and its arguments (adjuncts) in a sentence, I will focus on the problem of identifying what is the role of each one. For instance, the roles of "in May" and "in Madrid" are different (temporal vs. location), even if the syntactic position and prepositions are the same. At present the best technology uses training data, but given the sparse data available, we might find arguments that did not occur in the training data (sparseness problem). We mitigate this problem using models that integrate automatically learned selectional preferences, which encode what kind of entities verbs and prepositions expect in each role, e.g. the preposition "in" expects time expresions for the temporal role, and locations for the locations role. We explore a range of models based on WordNet and distributional-similarity. Furthermore, we demonstrate that the SRC task is better modeled by SP models centered on both verbs and prepositions, rather than verbs alone.