most citedReasoning with Latent Structure Refinement for Document-Level Relation Extraction

28 citations · 63 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV202014 cited

RPT: Learning Point Set Representation for Siamese Visual Tracking

Ziang Ma, Linyuan Wang, Haitao Zhang +2

While remarkable progress has been made in robust visual tracking, accurate target state estimation still remains a highly challenging problem. In this paper, we argue that this is…

cs.CL202028 cited

Reasoning with Latent Structure Refinement for Document-Level Relation Extraction

Guoshun Nan, Zhijiang Guo, Ivan Sekulić +1

Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence ent…

cs.SI2020

Adversarial Deep Network Embedding for Cross-network Node Classification

Xiao Shen, Quanyu Dai, Fu-lai Chung +2

In this paper, the task of cross-network node classification, which leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network,…

cs.CL2020

Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model

Guoxiu He, Zhe Gao, Zhuoren Jiang +4

The nonliteral interpretation of a text is hard to be understood by machine models due to its high context-sensitivity and heavy usage of figurative language. In this study, inspir…

cs.IT20195 cited

The Parameters of Minimal Linear Codes

Wei Lu, Xia Wu, Xiwang Cao

Let be two positive integers and a prime power. The basic question in minimal linear codes is to determine if there exists an minimal linear code. The first…

stat.ME201916 cited

RCRnorm: An integrated system of random-coefficient hierarchical regression models for normalizing NanoString nCounter data

Gaoxiang Jia, Xinlei Wang, Qiwei Li +4

Formalin-fixed paraffin-embedded (FFPE) samples have great potential for biomarker discovery, retrospective studies and diagnosis or prognosis of diseases. Their application, howev…