833 citations · 2k across the 50 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Automatic Synonym Discovery with Knowledge Bases
Meng Qu, Xiang Ren, Jiawei Han
Recognizing entity synonyms from text has become a crucial task in many entity-leveraging applications. However, discovering entity synonyms from domain-specific text corpora (e.g.…
MetaPAD: Meta Pattern Discovery from Massive Text Corpora
Meng Jiang, Jingbo Shang, Taylor Cassidy +4
Mining textual patterns in news, tweets, papers, and many other kinds of text corpora has been an active theme in text mining and NLP research. Previous studies adopt a dependency…