The Anaphora Resolution task of Evalita 2011 was intended to measure the ability of participating systems to recognize mentions of the same real-world entity within a given document. The UNIPI system is based on the analysis of dependency parse trees and on similarity clustering. Mention detection relies on parse trees obtained by re-parsing texts with DeSR, and on ad-hoc heuristics to deal with specific cases, when mentions boundaries do not correspond to sub-trees. A binary classifier, based on Maximum Entropy, is used to decide whether there is a coreference relationship between each pair of mentions detected in the previous phase. Clustering of entities is performed by a greedy clustering algorithm.
UNIPI Participation in the Evalita 2011 Anaphora Resolution Task
ATTARDI, GIUSEPPE;SIMI, MARIA
2012-01-01
Abstract
The Anaphora Resolution task of Evalita 2011 was intended to measure the ability of participating systems to recognize mentions of the same real-world entity within a given document. The UNIPI system is based on the analysis of dependency parse trees and on similarity clustering. Mention detection relies on parse trees obtained by re-parsing texts with DeSR, and on ad-hoc heuristics to deal with specific cases, when mentions boundaries do not correspond to sub-trees. A binary classifier, based on Maximum Entropy, is used to decide whether there is a coreference relationship between each pair of mentions detected in the previous phase. Clustering of entities is performed by a greedy clustering algorithm.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
