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http://dx.doi.org/10.15207/JKCS.2018.9.11.023

Meta Learning based Global Relation Extraction trained by Traditional Korean data  

Kim, Kuekyeng (Department of Computer Science and Engineering, Korea University)
Kim, Gyeongmin (Department of Computer Science and Engineering, Korea University)
Jo, Jaechoon (Department of Computer Science and Engineering, Korea University)
Lim, Heuiseok (Department of Computer Science and Engineering, Korea University)
Publication Information
Journal of the Korea Convergence Society / v.9, no.11, 2018 , pp. 23-28 More about this Journal
Abstract
Recent approaches to Relation Extraction methods mostly tend to be limited to mention level relation extractions. These types of methods, while featuring high performances, can only extract relations limited to a single sentence or so. The inability to extract these kinds of data is a terrible amount of information loss. To tackle this problem this paper presents an Augmented External Memory Neural Network model to enable Global Relation Extraction. the proposed model's Global relation extraction is done by first gathering and analyzing the mention level relation extraction by the Augmented External Memory. Additionally the proposed model shows high level of performances in korean due to the fact it can take the often omitted subjects and objectives into consideration.
Keywords
Relation Extraction; Augmented Memory Neural Networks; Meta Learning; Text summarization; Natural language Processing; Machine Learning;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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