This paper summarizes the participation of UNIMIB team in the Named Entity rEcognition and Linking (NEEL) Challenge in #Microposts2016. In this paper, we propose a knowledge-base approach for identifying and linking named entities from tweets. The named entities are, further, classified using evidence provided by our entity linking algorithm and type-casted into Microposts categories.
Caliano, D., Fersini, E., Manchanda, P., Palmonari, M., Messina, V. (2016). UniMiB: Entity linking in tweets using Jaro-Winkler distance, popularity and coherence. In Proceedings of the 6th Workshop on 'Making Sense of Microposts' co-located with the 25th International World Wide Web Conference {(WWW} 2016 (pp.70-72). CEUR-WS.
UniMiB: Entity linking in tweets using Jaro-Winkler distance, popularity and coherence
FERSINI, ELISABETTASecondo
;MANCHANDA, PIKAKSHI;PALMONARI, MATTEO LUIGIPenultimo
;MESSINA, VINCENZINAUltimo
2016
Abstract
This paper summarizes the participation of UNIMIB team in the Named Entity rEcognition and Linking (NEEL) Challenge in #Microposts2016. In this paper, we propose a knowledge-base approach for identifying and linking named entities from tweets. The named entities are, further, classified using evidence provided by our entity linking algorithm and type-casted into Microposts categories.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
