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Adding an inception network to neural network open information extraction
journal contribution
posted on 2023-05-21, 07:20 authored by Duy Van LeDuy Van Le, Kenneth KirkbyKenneth Kirkby, Erin MontgomeryErin Montgomery, Joel ScanlanJoel ScanlanThis paper presents a method to resolve tuples from plain text by adding an inception network, and dependency path embedding to existing neural network methods of Open Information Extraction (Open IE). Inception networks are used in analysis of computer vision, and dependency path embedding in text processing, but neither has been reported with Open IE. Performance was measured on benchmark datasets using two existing Open IE deep learning methods, one using bidirectional long short-term memory and BIO tagging (RnnOIE-verb), and another using a span-based model (SpanOIE). RnnOIE-verb was compared with RnnOIE-verb plus inception network and/or dependency path embedding. SpanOIE was compared with SpanOIE plus inception network. Performance slightly increased with the addition of inception network to RnnOIE-verb (before AUC 0.45, F1 0.59; after AUC 0.46, F1 0.60) and inception network to SpanOIE (before AUC 0.63, F1 0.748; after AUC 0.64, F1 0.764). The performance gain was minor but potentially relevant to an iterative process of improvement.
History
Publication title
IEEE Intelligent SystemsVolume
37Pagination
85-97ISSN
1541-1672Department/School
School of Information and Communication TechnologyPublisher
Institute of Electrical and Electronics EngineersPlace of publication
United StatesRights statement
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