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• W2184639959 abstract "Relation extraction is a fundamental task in information extraction, which is to identify the semantic relationships between two entities in the text. In this paper, deep belief nets (DBN), which is a classifier of a combination of several unsupervised learning networks, named RBM (restricted Boltzmann machine) and a supervised learning network named BP (back-propagation), is presented to detect and classify the relationships among Chinese name entities. The RBM layers maintain as much information as possible when feature vectors are transferred to next layer. The BP layer is trained to classify the features generated by the last RBM layer. The experiments are conducted on the Automatic Content Extraction 2004 dataset. This paper proves that a character-based feature is more suitable for Chinese relation extraction than a word-based feature. In addition, the paper also performs a set of experiments to assess the Chinese relation extraction on different assumptions of an entity categorization feature. These experiments showed the comparison among models with correct entity types and imperfect entity type classified by DBN and without entity type. The results show that DBN is a successful approach in the high-dimensional-feature-space information extraction task. It outperforms state-of-the-art learning" @default.
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• W2184639959 date "2012-01-01" @default.
• W2184639959 modified "2023-09-24" @default.
• W2184639959 title "Chinese Relation Extraction Based on Deep Belief Nets" @default.
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