The effects of sampling and semantic categories on large-scale supervised relation extraction
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2012
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Melbourne Authors
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Department of Computing and Information Systems, Engineering
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PhD thesis
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Willy (2012). The effects of sampling and semantic categories on large-scale supervised relation extraction. PhD thesis, Department of Computing and Information Systems, Engineering, The University of Melbourne.
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© 2012 Dr. Willy
Abstract
The purpose of relation extraction is to identify novel pairs of entities which are related by a pre-specified relation such as hypernym or synonym. The traditional approach to relation extraction is to building a dedicated system for a particular relation, meaning that significant effort is required to repurpose the approach to new relations. We propose a generic approach based on supervised learning, which provides a standardised process for performing relation extraction on different relations and domains. We explore the feasibility of the approach over a range of relations and corpora, focusing particularly on the development of a realistic evaluation methodology for relation extraction. In addition to this, we investigate the impact of semantic categories on extraction effectiveness.
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relation extraction, semantic categories, information extraction, natural language processing, computational linguistics, machine learning, sampling