PUB - Publikationen an der Universität Bielefeld

Named Entity Linking (NEL) is the task of disambiguating named entities in a natural language text by linking them to their corresponding entities in a knowledge base such as DBpedia. Thus, it is an important step in transforming unstructured text into structured knowledge. Previous work on this task has proven a strong impact of graph-based methods such as PageRank on entity linking. Other approaches rely on distributional similarity between an article and the textual description of a candidate entity. However, the combined impact of these different feature groups has not been explored to a sufficient extent. In this paper, we present a novel approach that exploits a unified probabilistic model based on undirected probabilistic models to combine different types of features for named entity linking. Capitalizing on Markov Chain Monte Carlo sampling, our model is capable of exploiting complementary strengths between both graph-based and textual features. We analyze the impact of these features and their combination on named entity linking to enhance our understanding of the task. In an evaluation on several data sets from the GERBIL benchmark, our model compares favourably with the current state-of-the-art. On the AIDA/CoNLL data set, in particular, we outperform previous approaches.