A large number of web queries are related to product entities. Studying evolution of product entities can help analysts understand the change in particular attribute values for these products. However, studying the evolution of a
\nproduct requires us to be able to link various versions of a product together in a temporal order. While it is easy to temporally link recent versions of products in a few domains manually, solving the problem in general is challenging. The ability to temporally order and link various versions of a single product can also improve product search engines. In this paper, we tackle the problem of \ffinding the previous version (predecessor) of a product entity. Given a repository of product entities, we \ffirst parse the product names using a CRF model. After identifying entities corresponding to a\u00a0 single product, we solve the problem of \ffinding the previous version of any given particular version of the product. For the second task, we leverage innovative features with a Na\u0010ve Bayes classifi\fer. Our methods achieve a precision of \u001888% in identifying the product version from product entity names, and a precision of \u001853% in identifying the predecessor.<\/p>\n","protected":false},"excerpt":{"rendered":"
A large number of web queries are related to product entities. Studying evolution of product entities can help analysts understand the change in particular attribute values for these products. However, studying the evolution of a product requires us to be able to link various versions of a product together in a temporal order. 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