A Hybrid Approach for Entity Recognition and Linking

A hybrid approach is presented, which combines the strength of a linguistic-based method augmented by a high coverage in the annotation obtained by using a large knowledge base as entity dictionary, to improve the extraction and recognition level to get the best recall in order to apply a pruning step.

Abstract

Numerous research efforts are tackling the entity recognition and entity linking tasks resulting in a large body of literature. One could roughly categorize the proposed approaches in two different strategies: linguistic-based and semantic-based methods. In this paper, we present our participation to the OKE challenge, where we experiment with a hybrid approach, which combines the strength of a linguistic-based method augmented by a high coverage in the annotation obtained by using a large knowledge base as entity dictionary. The main goal of this hybrid approach is to improve the extraction and recognition level to get the best recall in order to apply a pruning step. On the training set, the results are promising and the breakdown figures are comparable with the state of the art performance of top ranked systems. Our hybrid approach has been ranked first to the OKE Challenge on the test set.