Detección de menciones anidadas basada en expansión para el español

Mention detection is the first module used in coreference resolution systems. Due to that, it is important that the results obtained by this module are as high as possible. Within the field of mention detection, nested mentions are the most difficult ones to detect. In this paper, we present a nested mention detection system based on expansion, a new model for detecting nested elements in NLP based on machine learning. The results obtained by our system are above the 72% in F-measure in AnCora corpus. We can not compare directly our results with other systems, since there are not exist, but if we consider that the average F-measure obtained by other systems for all mention (not only nested mentions), and that we are dealing with the most difficult mentions, we achieve good results.