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Deep Belief Networks Based Toponym Recognition for Chinese Text

by 1,2,3, 1,2,3,*, 1,2,3 and 1,2,3
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(6), 217;
Received: 20 April 2018 / Revised: 21 May 2018 / Accepted: 12 June 2018 / Published: 14 June 2018
(This article belongs to the Special Issue Place-Based Research in GIScience and Geoinformatics)
In Geographical Information Systems, geo-coding is used for the task of mapping from implicitly geo-referenced data to explicitly geo-referenced coordinates. At present, an enormous amount of implicitly geo-referenced information is hidden in unstructured text, e.g., Wikipedia, social data and news. Toponym recognition is the foundation of mining this useful geo-referenced information by identifying words as toponyms in text. In this paper, we propose an adapted toponym recognition approach based on deep belief network (DBN) by exploring two key issues: word representation and model interpretation. A Skip-Gram model is used in the word representation process to represent words with contextual information that are ignored by current word representation models. We then determine the core hyper-parameters of the DBN model by illustrating the relationship between the performance and the hyper-parameters, e.g., vector dimensionality, DBN structures and probability thresholds. The experiments evaluate the performance of the Skip-Gram model implemented by the Word2Vec open-source tool, determine stable hyper-parameters and compare our approach with a conditional random field (CRF) based approach. The experimental results show that the DBN model outperforms the CRF model with smaller corpus. When the corpus size is large enough, their statistical metrics become approaching. However, their recognition results express differences and complementarity on different kinds of toponyms. More importantly, combining their results can directly improve the performance of toponym recognition relative to their individual performances. It seems that the scale of the corpus has an obvious effect on the performance of toponym recognition. Generally, there is no adequate tagged corpus on specific toponym recognition tasks, especially in the era of Big Data. In conclusion, we believe that the DBN-based approach is a promising and powerful method to extract geo-referenced information from text in the future. View Full-Text
Keywords: place names; Deep Belief Networks; toponym recognition; geographic information retrieval; Chinese text place names; Deep Belief Networks; toponym recognition; geographic information retrieval; Chinese text
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MDPI and ACS Style

Wang, S.; Zhang, X.; Ye, P.; Du, M. Deep Belief Networks Based Toponym Recognition for Chinese Text. ISPRS Int. J. Geo-Inf. 2018, 7, 217.

AMA Style

Wang S, Zhang X, Ye P, Du M. Deep Belief Networks Based Toponym Recognition for Chinese Text. ISPRS International Journal of Geo-Information. 2018; 7(6):217.

Chicago/Turabian Style

Wang, Shu, Xueying Zhang, Peng Ye, and Mi Du. 2018. "Deep Belief Networks Based Toponym Recognition for Chinese Text" ISPRS International Journal of Geo-Information 7, no. 6: 217.

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