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http://dx.doi.org/10.5392/JKCA.2019.19.11.278

Korean Spatial Information Extraction using Bi-LSTM-CRF Ensemble Model  

Min, Tae Hong (충북대학교 컴퓨터과학과)
Shin, Hyeong Jin (충북대학교 컴퓨터과학과)
Lee, Jae Sung (충북대학교 소프트웨어학과)
Publication Information
The Journal of the Korea Contents Association / v.19, no.11, 2019 , pp. 278-287 More about this Journal
Abstract
Spatial information extraction is to retrieve static and dynamic aspects in natural language text by explicitly marking spatial elements and their relational words. This paper proposes a deep learning approach for spatial information extraction for Korean language using a two-step bidirectional LSTM-CRF ensemble model. The integrated model of spatial element extraction and spatial relation attribute extraction is proposed too. An experiment with the Korean SpaceBank demonstrates the better efficiency of the proposed deep learning model than that of the previous CRF model, also showing that the proposed ensemble model performed better than the single model.
Keywords
Spatial Information; Information Extraction; Deep Learning; LSTM-CRF;
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