This paper describes the participation of the UNIBA team in the Named Entity rEcognition and Linking (NEEL) Challenge. We propose a knowledge-based 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 algorithm performs poorly in the entity recognition, while it achieves good results in the disambiguation step.
['Pierpaolo Basile', 'Annalina Caputo', 'Giovanni Semeraro', 'Fedelucio Narducci']
UNIBA: Exploiting a Distributional Semantic Model for Disambiguating and Linking Entities in Tweets.