Linking entities in tweets to wikipedia knowledge base

Abstract

Entity linking has received much more attention. The purpose of entity linking is to link the mentions in the text to the corresponding entities in the knowledge base. Most work of entity linking is aiming at long texts, such as BBS or blog. Microblog as a new kind of social platform, entity linking in which will face many problems. In this paper, we divide the entity linking task into two parts. The first part is entity candidates’ generation and feature extraction. We use Wikipedia articles information to generate enough entity candidates, and as far as possible eliminate ambiguity candidates to get higher coverage and less quantity. In terms of feature, we adopt belief propagation, which is based on the topic distribution, to get global feature. The experiment results show that our method achieves better performance than that based on common links. When combining global features with local features, the performance will be obviously improved. The second part is entity candidates ranking. Traditional learning to rank methods have been widely used in entity linking task. However, entity linking does not consider the ranking order of non-target entities. Thus, we utilize a boosting algorithm of non-ranking method to predict the target entity, which leads to 77.48% accuracy.