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Learning Embeddings for Academic Papers

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  • Wed, 12/06/2017 - 1:30pm - 3:30pm




Learning Embeddings for Academic Papers

PhD. Comprehensive Exam by:

Yi Zhang

Date:   Wednesday December 6th, 2017
Time: 1:30pm – 3:30pm
Location: LT 3105

Academic papers contain not only plain text, but also hyperlinks such as citations and references. Learning the vector representation from such dataset is very useful for many downstream tasks such as clustering, classification, information retrieval, and recommendation system.

Recently, predict-based algorithms have been successfully applied on learning the embeddings of words, documents, and networks. However, only a few works focus on learning the representation of linked documents such as academic papers.

In this presentation, we first introduce some state-of-art embedding algorithms for words, documents, and networks. Then we discuss the limitation of existing methods as well as the solutions.

Thesis Committee:    
Internal Reader: Dr. Mehdi Kargar, Dr. Dan Wu     
External Reader: Dr. Jonathan Wu (ECE)
Advisor: Dr. Jianguo Lu



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