Resolving Person Names in Web People Search
Disambiguating person names in a set of documents (such as a set of web pages returned in response to a person name) is a key task for the presentation of results and the automatic profiling of experts. With largely unstructured documents and an unknown number of people with the same name the problem presents many difficulties and challenges. This chapter treats the task of person name disambiguation as a document clustering problem, where it is assumed that the documents represent particular people. This leads to the person cluster hypothesis, which states that similar documents tend to represent the same person. Single Pass Clustering, k-Means Clustering, Agglomerative Clustering and Probabilistic Latent Semantic Analysis are employed and empirically evaluated in this context. On the SemEval 2007 Web People Search it is shown that the person cluster hypothesis holds reasonably well and that the Single Pass Clustering and Agglomerative Clustering methods provide the best performance.
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