Named entity disambiguation based on explicit semantics

Abstract

In our work we present an approach to the Named Entity Disambiguation based on semantic similarity measure. We employ existing explicit semantics present in datasets such as Wikipedia to construct a disambiguation dictionary and vector-based word model. The analysed documents are transformed into semantic vectors using explicit semantic analysis. The relatedness is computed as cosine similarity between the vectors. The experimental evaluation shows that the proposed approach outperforms traditional approaches such as latent semantic analysis. © 2012 Springer-Verlag.