DeepEncyclolink: A Cross-Encyclopedia, Cross-language Article-Linking System Based on Deep Learning
Authors: 
Ka Ming Wong, Chia-Min Chuang, Chun-Kai Wu, Yu-Chun Wang, Richard Tzong-Han TsaiAuthors Info & Claims
Ka Ming Wong, Chia-Min Chuang, Chun-Kai Wu, Yu-Chun Wang, Richard Tzong-Han TsaiAuthors Info & ClaimsPages 116 - 117
Abstract
DeepEncyclolink is a web-based system for linking pairs of corresponding articles from different encyclopedias in different languages. The core technology of DeepEncyclolink is based on paragraph embeddings calculated by a long-short-term-memory network with attention. Compared to our previous feature-based machine learning model, DeepEncyclolink has made great strides in coverage and accuracy. DeepEncyclolink offers an easy-to-use user interface and is supported by a powerful service backend for retrieving and selecting equivalent articles between English Wikipedia and Chinese Baidu Encyclopedia. DeepEncyclolink breaks the barriers of different encyclopedias and different languages, making it easy for users to access knowledge written in various languages.
References
[1]
M. Beaulieu, M. Gatford, X. Huang, S. Robertson, S. Walker, and P. Williams. 1997. Okapi at TREC-5. Proceedings of the fifth Text REtrieval Conference (TREC-5) (1997), 143–166.
[2]
Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882(2014).
[3]
Aliaksei Severyn and Alessandro Moschitti. 2015. Learning to rank short text pairs with convolutional deep neural networks. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. ACM, 373–382.
[4]
Avirup Sil, Gourab Kundu, Radu Florian, and Wael Hamza. 2018. Neural cross-lingual entity linking. In Thirty-Second AAAI Conference on Artificial Intelligence.
[5]
Duyu Tang, Bing Qin, and Ting Liu. 2015. Document modeling with gated recurrent neural network for sentiment classification. In Proceedings of the 2015 conference on empirical methods in natural language processing. 1422–1432.
[6]
Yu-Chun Wang, Chun-Kai Wu, and Richard Tzong-Han Tsai. 2014. Cross-language and cross-encyclopedia article linking using mixed-language topic model and hypernym translation. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 586–591.
[7]
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. In Advances in neural information processing systems. 649–657.
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- DeepEncyclolink: A Cross-Encyclopedia, Cross-language Article-Linking System Based on Deep Learning
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Published In
April 2020
854 pages
ISBN:9781450370240
DOI:10.1145/3366424
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Published: 20 April 2020
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