27 citations · 109 across the 10 of their papers we have counts for
12 papers
Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning
Meng Qu, Xiang Ren, Yu Zhang +1
Extracting relations from text corpora is an important task in text mining. It becomes particularly challenging when focusing on weakly-supervised relation extraction, that is, uti…
Graph Clustering with Dynamic Embedding
Carl Yang, Mengxiong Liu, Zongyi Wang +2
Graph clustering (or community detection) has long drawn enormous attention from the research on web mining and information networks. Recent literature on this topic has reached a…
Indirect Supervision for Relation Extraction using Question-Answer Pairs
Zeqiu Wu, Xiang Ren, Frank F. Xu +2
Automatic relation extraction (RE) for types of interest is of great importance for interpreting massive text corpora in an efficient manner. Traditional RE models have heavily rel…
Unsupervised Extraction of Representative Concepts from Scientific Literature
Adit Krishnan, Aravind Sankar, Shi Zhi +1
This paper studies the automated categorization and extraction of scientific concepts from titles of scientific articles, in order to gain a deeper understanding of their key contr…
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
Meng Qu, Jian Tang, Jingbo Shang +3
Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches u…
Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach
Liyuan Liu, Xiang Ren, Qi Zhu +4
Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-co…