Rol semantikoen etiketatze automatikoarol multzoak eta hautapen murriztapenak

  1. Zapirain Sierra, Beñat
unter der Leitung von:
  1. Lluís Márquez Villodre Doktorvater/Doktormutter
  2. Eneko Agirre Bengoa Doktorvater/Doktormutter

Universität der Verteidigung: Universidad del País Vasco - Euskal Herriko Unibertsitatea

Fecha de defensa: 23 von Februar von 2011

Gericht:
  1. Inmaculada Hernáez Rioja Präsident/in
  2. Arantza Díaz de Ilarraza Sánchez Sekretär/in
  3. Xavier Carreras Vocal
  4. Andoni Sagarna Izaguirre Vocal
  5. Roser Morante Vocal
Fachbereiche:
  1. Lenguajes y Sistemas Informáticos

Art: Dissertation

Teseo: 305803 DIALNET lock_openADDI editor

Zusammenfassung

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.