activity
20172020
most citedLearning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction

5 citations · 10 across the 4 of their papers we have counts for

collaborators

6 papers

cs.IR2020

On Disambiguating Authors: Collaboration Network Reconstruction in a Bottom-up Manner

Na Li, Renyu Zhu, Xiaoxu Zhou +4

Author disambiguation arises when different authors share the same name, which is a critical task in digital libraries, such as DBLP, CiteULike, CiteSeerX, etc. While the state-of-…

cs.CL20205 cited

Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction

Yixin Cao, Jun Kuang, Ming Gao +3

Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts.However, it usually suffers from the long-tail issue. The traini…

cs.CV20202 cited

EDSL: An Encoder-Decoder Architecture with Symbol-Level Features for Printed Mathematical Expression Recognition

Yingnan Fu, Tingting Liu, Ming Gao +1

Printed Mathematical expression recognition (PMER) aims to transcribe a printed mathematical expression image into a structural expression, such as LaTeX expression. It is a crucia…

cs.CL2019

Improving Neural Relation Extraction with Implicit Mutual Relations

Jun Kuang, Yixin Cao, Jianbing Zheng +3

Relation extraction (RE) aims at extracting the relation between two entities from the text corpora. It is a crucial task for Knowledge Graph (KG) construction. Most existing metho…

cs.SI2019

Learning Vertex Representations for Bipartite Networks

Ming Gao, Xiangnan He, Leihui Chen +3

Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks li…

cs.IR20173 cited

BiRank: Towards Ranking on Bipartite Graphs

Xiangnan He, Ming Gao, Min-Yen Kan +1

The bipartite graph is a ubiquitous data structure that can model the relationship between two entity types: for instance, users and items, queries and webpages. In this paper, we…