activity
20182022
most citedThree Sentences Are All You Need: Local Path Enhanced Document Relation Extraction

5 citations · 7 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL20221 cited

Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED

Quzhe Huang, Shibo Hao, Yuan Ye +3

DocRED is a widely used dataset for document-level relation extraction. In the large-scale annotation, a \textit{recommend-revise} scheme is adopted to reduce the workload. Within…

cs.CL20215 cited

Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction

Quzhe Huang, Shengqi Zhu, Yansong Feng +3

Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences. Yet, human annotators usually use a smal…

cs.CL20191 cited

Integrating Relation Constraints with Neural Relation Extractors

Yuan Ye, Yansong Feng, Bingfeng Luo +2

Recent years have seen rapid progress in identifying predefined relationship between entity pairs using neural networks NNs. However, such models often make predictions for each en…

cs.CV2019

SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines

Yinda Xu, Zeyu Wang, Zuoxin Li +2

Visual tracking problem demands to efficiently perform robust classification and accurate target state estimation over a given target at the same time. Former methods have proposed…

cs.CL2018

Analysis of Bag-of-n-grams Representation's Properties Based on Textual Reconstruction

Qi Huang, Zhanghao Chen, Zijie Lu +1

Despite its simplicity, bag-of-n-grams sen- tence representation has been found to excel in some NLP tasks. However, it has not re- ceived much attention in recent years and fur- t…