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Poster Submissions
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Cross-Media Knowledge Aquisition in X-Media Project
Joao Magalhaes, Jose' Iria, Lei Xi, Mark Greenwood, Fabio Ciravegna |
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Humans can easily understand information represented in different media. Articles, books and other formatted documents fit our mental process of acquiring knowledge: split information into meaningful parts and follow a logical flow or structure of ideas. We can understand an idea if it is written in scrap paper, in a formal letter, in an image, or even if the image is used to disambiguate the text. Unfortunately information processing systems are not comparable to humans in this task: information extraction from multiple document formats is usually a nightmare addressed by a vast number different libraries; information extraction have always focused on single-media data and ignore the structure of a document. In this context, the objective of the proposed Cross-Media Knowledge Acquisition framework addresses the following issues: (1) process information from different formats and media-types; (2) design cross-media features extractors that explore the rich structure of documents; (3) algorithms capable of exploiting heterogenous sources of data; (4) use background knowledge to improve information extraction.
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