Loopy belief propagation for large scale collective entity linking
Entity linking, a very popular research topic nowadays, involves identifying mentions of 'real world' entities in natural language text and associating them with a representation from a knowledge thesaurus. It is a form of semantic grounding of discourse in a background repository of world knowledge and therefore a key step towards deep language understanding. Applications of this research area include semantic search, automatic document enrichment, ontology learning, relation extraction, question answering, etc. An entity linking pipeline deals with two problems: first, identifying token spans that should be linked and, second, selecting the corresponding entity from an entity catalogue. Traditionally, local approaches that make independent entity linking decisions have been proposed. They are largely based on the local compatibility between the context of the mention and the textual metadata of the entity. These techniques are limited because they do not make use of the underlying entities that co occur in the text. Alternatively, collective entity linking is a better solution that resolves all names in a document simultaneously. This is motivated by the fact that in a coherent document co occurring entities are typically semantically related. Inferring the best global assignment of entities requires searching through an exponential number of entity resolution combinations. The resulting MAP inference problem can be NP hard and therefore approximations or pruning techniques are used. In this thesis we propose solving this MAP inference problem via loopy belief propagation for entity disambiguation. We model the joint distribution through log linear models and compare with state of the art techniques. We assume that frequent co occurrence of entity pairs implies semantic relatedness. We then use collected statistics from a corpus to make an informed decision. Exploiting calibrated pairwise and single node potentials leads to better results while comparing to a recently proposed algorithm: a random graph walk method.