Towards large-scale, open-domain and ontology-based named entity classification
Philipp Cimiano, Johanna Völker · Publikationen an der Universität Bielefeld (Universität Bielefeld) · 2005
Named entity recognition and classification research has so far mainly focused on supervised techniques and has typically considered only small sets of classes with regard to which to classify the recognized entities.In this paper we address the classification of named entities with regard to large sets of classes which are specified by a given ontology.Our approach is unsupervised as it relies on no labeled training data and is open-domain as the ontology can simply be exchanged.The approach is based on Harris' distributional hypothesis and, based on the vector-space model, it assigns a named entity to the contextually most similar concept from the ontology.The main contribution of the paper is a systematic analysis of the impact of varying certain parameters on such a context-based approach exploiting similarities in vector space for the disambiguation of named entities.