Automatic acquisition of qualia roles using an Italian semantically annotated corpus

This thesis investigates and improves a machine learning approach which permits to automatically recognize agentive and telic roles for nouns from a large CoNLL parsed Italian corpus. Following the work of Yamada and Baldwin (Yamada and Baldwin, 2004) I use a token-level supervised classifier to dynamically discover for each noun of a set a number of verbs considered as being the agentive or telic role of that noun. Furthermore, after the creation of three semantically different training sets, corresponding to three subclasses of the SimpleOWL ontology (Location, Artifact and LivingEntity), I run two different experiments to evaluate whether selecting positive and negative instances among those belonging to the same semantic group can improve the results or not and to understand whether semantic features are useful for this goal. The research lies on the assumption of the compositional nature of natural language, as described in the Generative Lexicon (GL) theory by Pustejovski, and in particular on the importance of the so called qualia roles. Lexica based on GL can be used in many different computational linguistic tasks such as Question answering and Textual Entailment, but they are extremely time consuming and expensive to develop manually.