Name Disambiguation Boosted by Latent Topics from Web Directories

Search results for personal name queries often contain documents relevant to several people as a personal name is often shared by several people. In order to differentiate people in these search results, it is required to extract contexts relevant to people in documents. However, since Web documents are noisy and the texts related to people might be short, it is difficult to extract contexts of people effectively. We propose a new method that uses web directories as additional information in order to recognize topic terms in documents more easily and to extract contexts of people more effectively. First, we apply latent Dirichlet allocation method to extract latent topics in Web directories. Then, the extracted topics are used to recognize topics contained in name ambiguity documents so that common context measurements can be calculated more effectively. Our experiments, conducted with documents of real people in the Web and several well-known Web directories, show that our approach disambiguates personal names better than some other conventional approaches like vector space model approach and named entity recognition approach.