Rol semantikoen etiketatze automatikoarol multzoak eta hautapen murriztapenak

  1. Zapirain Sierra, Beñat
Supervised by:
  1. Lluís Márquez Villodre Director
  2. Eneko Agirre Bengoa Director

Defence university: Universidad del País Vasco - Euskal Herriko Unibertsitatea

Fecha de defensa: 23 February 2011

  1. Inmaculada Hernáez Rioja Chair
  2. Arantza Díaz de Ilarraza Sánchez Secretary
  3. Xavier Carreras Committee member
  4. Andoni Sagarna Izaguirre Committee member
  5. Roser Morante Committee member
  1. Lenguajes y Sistemas Informáticos

Type: Thesis

Teseo: 305803 DIALNET lock_openADDI editor


This thesis focuses on two well-known open issues in Semantic Role Classi fication (SRC) research: (1) the suitability of diferent role inventories in practice, and (2) the limited in uence and sparseness of lexical features. About the former, we present an empirical comparative study on the use of PropBank vs. VerbNet roles, the two most widely used role inventories, testing the performance diferences for unseen verbs and the robustness for new corpus domains. About the latter, we test the use of automatically learnt selectional preferences as a complement to lexical features, proposing both WordNet-based and distributional similarity based models. We show that all our selectional preference models improve over lexical features in in-vitro experiments, and that the models are complementary. Finally, we show that incorporating features based on selectional preferences, the overall performance of an state-of-the-art SRC system improves both in in-domain and out-of-domain corpora.