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
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.File in questo prodotto:
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