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• W1537030134 abstract "There are vast amounts of free text on the internet that are neither grammatical nor formally structured, such as item descriptions on Ebay or internet classifieds like Craig's list. These sources of data, called posts, are full of useful information for agents scouring the Semantic Web, but they lack the semantic annotation to make them searchable. Annotating these posts is difficult since the text generally exhibits little formal grammar and the structure of the posts varies. However, by leveraging collections of known entities and their common attributes, called sets, we can annotate these posts despite their lack of grammar and structure. To use this reference data, we align a post to a member of the reference set, and then exploit this matched member during information extraction. We compare this extraction approach to more traditional information extraction methods that rely on structural and grammatical characteristics, and we show that our approach outperforms traditional methods on this type of data." @default.
• W1537030134 created "2016-06-24" @default.
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• W1537030134 date "2005-07-30" @default.
• W1537030134 modified "2023-09-25" @default.
• W1537030134 title "Semantic annotation of unstructured and ungrammatical text" @default.
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