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This 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.

Item Type:	Refereed Article
Keywords:	natural language processing, open information extraction, neural networks, inception network, predictive models
Research Division:	Information and Computing Sciences
Research Group:	Artificial intelligence
Research Field:	Natural language processing
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Artificial intelligence
UTAS Author:	Le, DV (Mr Van Duy Le)
UTAS Author:	Kirkby, K (Professor Kenneth Kirkby)
UTAS Author:	Montgomery, J (Dr James Montgomery)
UTAS Author:	Scanlan, J (Dr Joel Scanlan)
ID Code:	149876
Year Published:	2022
Deposited By:	Information and Communication Technology
Deposited On:	2022-04-21
Last Modified:	2022-06-09
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