activity
20222024
most citedGraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search

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

collaborators

5 papers

cs.CL2024

Generative Sentiment Analysis via Latent Category Distribution and Constrained Decoding

Jun Zhou, Dongyang Yu, Kamran Aziz +4

Fine-grained sentiment analysis involves extracting and organizing sentiment elements from textual data. However, existing approaches often overlook issues of category semantic inc…

cs.CL2024

Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm

Qiang Gao, Zixiang Meng, Bobo Li +4

Document-level event extraction aims to extract structured event information from unstructured text. However, a single document often contains limited event information and the rol…

cs.CL20241 cited

CMNER: A Chinese Multimodal NER Dataset based on Social Media

Yuanze Ji, Bobo Li, Jun Zhou +3

Multimodal Named Entity Recognition (MNER) is a pivotal task designed to extract named entities from text with the support of pertinent images. Nonetheless, a notable paucity of da…

cs.CV2023

Dynamic Clustering Transformer Network for Point Cloud Segmentation

Dening Lu, Jun Zhou, Kyle Yilin Gao +4

Point cloud segmentation is one of the most important tasks in computer vision with widespread scientific, industrial, and commercial applications. The research thereof has resulte…

cs.LG20224 cited

GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search

Xinshun Feng, Herun Wan, Shangbin Feng +4

Current Graph Neural Networks (GNNs) suffer from the over-smoothing problem, which results in indistinguishable node representations and low model performance with more GNN layers.…