activity
20152017
most citedLINE: Large-scale Information Network Embedding

4.8k citations · 4.8k across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2017

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…

cs.SI201727 cited

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…

cs.CL201713 cited

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.…

cs.CL2016

Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding

Xiang Ren, Wenqi He, Meng Qu +3

Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions. However, t…

cs.LG20154.8k cited

LINE: Large-scale Information Network Embedding

Jian Tang, Meng Qu, Mingzhe Wang +3

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classifica…