MAtheses submitted to and defended at any University in Italy within the yearly time frame specified in the call are eligible for the prize. The thesis should address a topic in computational linguistics or its applications, and may be written in Italian or English. The sub-areas involved are those listed in the yearly call for papers of the Italian Conference on Computational Linguistics (CLiC-it).
The candidates’ works are evaluated by a jury composed by three members: one of the co-chairs of the previous CLiC-it conference, one co-chair of the current CLiC-it conference (who agrees to serve for two years, so as to ensure continuity), and a member of the board of AILC.
The prize consists of 500 euros and the author will have the chance to present their theses at the upcoming CLiC-it conference.
Starting from the 2020 edition, the AILC best Master thesis award is dedicated to Emanuele Pianta, researcher at Fondazione Bruno Kessler (FBK) and director of Centro per la Valutazione delle Tecnologie del Linguaggio e della Comunicazione (CELCT), who passed away in November 2012. AILC acknowledges Emanuele Pianta’s important role in opening up research directions based on both the linguistic study of phenomena and their computational modeling, implementing solutions that are presently still appreciated. We think that Emanuele’s attention towards interdisciplinary research well represents the spirit of AILC, that originated as an initiative that should include all the various aspects of computational linguistics in Italy.
List of awardees:
- 2020: Andrea Santilli (University of Roma Tor Vergata) Continual Language Learning with Syntax-based Episodic Memory in Neural Networks (Advisor: Fabio Massimo Zanzotto)
- 2019: Ludovica Pannitto (University of Pisa). Event Knowledge in Compositional Distributional Semantics (Advisor: Alessandro Lenci)
- 2018: Enrica Troiano (University of Trento / FBK). A Computational Study of Linguistic Exaggerations (Advisor: Carlo Strapparava)
- 2017: Alessio Miaschi (University of Pisa). Definizione di modelli computazionali per lo studio dell’evoluzione delle abilità di scrittura a partire da un corpus di produzioni scritte di apprendenti della scuola secondaria di primo grado (Advisor: Felice Dell’Orletta)