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
2015-01-01
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.
