Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/14080
Title: Document clustering on target entities using persons and organizations
Authors: KEI JEHN MING, JEREMY RAPHAEL
Keywords: web clustering, information retrieval, machine learning
Issue Date: 5-Sep-2004
Source: KEI JEHN MING, JEREMY RAPHAEL (2004-09-05). Document clustering on target entities using persons and organizations. ScholarBank@NUS Repository.
Abstract: Web surfing often involves carrying out information finding tasks using online search engines. These searches often contain keywords that are names, as in the case of Persons and Organizations (abbreviated a??PnOsa??). Such names are often not distinctive, commonly occurring, and non-unique. Thus, a single name may be mapped to several named entities. The result is users having to sift through mountains of pages and put together manually a set of information pertaining to the target entity in query.
URI: http://scholarbank.nus.edu.sg/handle/10635/14080
Appears in Collections:Master's Theses (Open)

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