document: Collective context-aware topic models for entity disambiguation A crucial step in adding structure to unstructured data is to identify references to entities and disambiguate them . Such disambiguated references can help enhance readability and draw similarities across different pieces of running text in an automated fashion . Previous research has tackled this problem by first forming a catalog of entities from a knowledge base , such as Wikipedia , and then using this catalog to disambiguate references in unseen text . However , most of the previously proposed models either do not use all text in the knowledge base , potentially missing out on discriminative features , or do not exploit word-entity proximity to learn high-quality catalogs . In this work , we propose topic models that keep track of the context of every word in the knowledge base ; so that words appearing within the same context as an entity are more likely to be associated with that entity . Thus , our topic models utilize all text present in the knowledge base and help learn high-quality catalogs . Our models also learn groups of co-occurring entities thus enabling collective disambiguation . Unlike most previous topic models , our models are non-parametric and do not require the user to specify the exact number of groups present in the knowledge base . In experiments performed on an extract of Wikipedia containing almost 60,000 references , our models outperform SVM-based baselines by as much as 18 % in terms of disambiguation accuracy translating to an increment of almost 11,000 correctly disambiguated references .
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