25 citations · 75 across the 12 of their papers we have counts for
8 papers · 1 filter
AE-smnsMLC: Multi-Label Classification with Semantic Matching and Negative Label Sampling for Product Attribute Value Extraction
Zhongfen Deng, Wei-Te Chen, Lei Chen +1
Product attribute value extraction plays an important role for many real-world applications in e-Commerce such as product search and recommendation. Previous methods treat it as a…
Electronic properties, correlated topology and Green's function zeros
Chandan Setty, Fang Xie, Shouvik Sur +3
There is extensive current interest about electronic topology in correlated settings. In strongly correlated systems, contours of Green's function zeros may develop in frequency-mo…
A survey on deep learning approaches for data integration in autonomous driving system
Xi Zhu, Likang Wang, Caifa Zhou +3
The perception module of self-driving vehicles relies on a multi-sensor system to understand its environment. Recent advancements in deep learning have led to the rapid development…
Identical and Fraternal Twins: Fine-Grained Semantic Contrastive Learning of Sentence Representations
Qingfa Xiao, Shuangyin Li, Lei Chen
The enhancement of unsupervised learning of sentence representations has been significantly achieved by the utility of contrastive learning. This approach clusters the augmented po…
Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading Comprehension
Tongtao Ling, Lei Chen, Huangxu Sheng +2
The traditional way of sentence-level event detection involves two important subtasks: trigger identification and trigger classifications, where the identified event trigger words…
Evolutionary Verbalizer Search for Prompt-based Few Shot Text Classification
Tongtao Ling, Lei Chen, Yutao Lai +1
Recent advances for few-shot text classification aim to wrap textual inputs with task-specific prompts to cloze questions. By processing them with a masked language model to predic…
A Simple and Effective Framework for Strict Zero-Shot Hierarchical Classification
Rohan Bhambhoria, Lei Chen, Xiaodan Zhu
In recent years, large language models (LLMs) have achieved strong performance on benchmark tasks, especially in zero or few-shot settings. However, these benchmarks often do not a…
When to Pre-Train Graph Neural Networks? From Data Generation Perspective!
Yuxuan Cao, Jiarong Xu, Carl Yang +5
In recent years, graph pre-training has gained significant attention, focusing on acquiring transferable knowledge from unlabeled graph data to improve downstream performance. Desp…