UNIBA: Exploiting a Distributional Semantic Model for Disambiguating and Linking Entities in Tweets.
Pierpaolo Basile, Annalina Caputo, Giovanni Maria Semeraro, Fedelucio Narducci · CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) · 2015
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.