This paper describes the participation of the UNIBA team in the Named Entity rEcognition and Linking (NEEL) Challenge. We propose a completely unsupervised algorithm able to recognize and link named entities in English tweets. The approach combines the simple Lesk algorithm with information coming from both a distributional semantic model and usage frequency of Wikipedia concepts. The results show encouraging performance.
UNIBA: Exploiting a Distributional Semantic Model for Disambiguating and Linking Entities in Tweets / Basile, Pierpaolo; Caputo, Annalina; Semeraro, Giovanni; Narducci, Fedelucio. - ELETTRONICO. - 1395:(2015), pp. 15.62-15.63. (Intervento presentato al convegno 24th International Conference on the World Wide Web, WWW 2015 tenutosi a Firenze, Italy nel May 18-22, 2015).
UNIBA: Exploiting a Distributional Semantic Model for Disambiguating and Linking Entities in Tweets
Fedelucio Narducci
2015
Abstract
This paper describes the participation of the UNIBA team in the Named Entity rEcognition and Linking (NEEL) Challenge. We propose a completely unsupervised algorithm able to recognize and link named entities in English tweets. The approach combines the simple Lesk algorithm with information coming from both a distributional semantic model and usage frequency of Wikipedia concepts. The results show encouraging performance.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
