3 citations · 4 across the 3 of their papers we have counts for
4 papers
Multi-Scale Feature and Metric Learning for Relation Extraction
Mi Zhang, Tieyun Qian
Existing methods in relation extraction have leveraged the lexical features in the word sequence and the syntactic features in the parse tree. Though effective, the lexical feature…
Exploit Multiple Reference Graphs for Semi-supervised Relation Extraction
Wanli Li, Tieyun Qian
Manual annotation of the labeled data for relation extraction is time-consuming and labor-intensive. Semi-supervised methods can offer helping hands for this problem and have arous…
Seq2seq Translation Model for Sequential Recommendation
Ke Sun, Tieyun Qian
The context information such as product category plays a critical role in sequential recommendation. Recent years have witnessed a growing interest in context-aware sequential reco…
Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks
Tieyun Qian, Yile Liang, Qing Li
Matrix completion is a classic problem underlying recommender systems. It is traditionally tackled with matrix factorization. Recently, deep learning based methods, especially grap…