Collective search for concept disambiguation

Name ambiguity is a major problem in information retrieval: The name "Metropolis" may refer to a movie, a physicist, or Superman's hometown. Recent work resolves ambiguity in natural language text by linking name mentions against the corresponding Wikipedia concept (Wikification). Standard methods comparing a single mention with the corresponding Wikipedia concept can potentially be improved by simultaneously considering all mentions in the input document. We propose a novel multiple assignment process based on a collective search over an inverted index that exploits the coherence of Wikipedia concepts. Based on this coherence, we compute the best fitting candidate concept for each mention and combine it with context information in a second search step. Using additional attributes an SVM then re-ranks the result of this search and estimates if a concept is not covered in Wikipedia. We give a unified view over the different performance measures used in other state-of-the art approaches and evaluate our approach on five benchmark corpora. On these corpora, our method has the most stable performance yielding similar or better results compared to other approaches.