activity
20172022
most citedEmpower Sequence Labeling with Task-Aware Neural Language Model

151 citations · 723 across the 33 of their papers we have counts for

collaborators

60 papers

cs.AI202221 cited

TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters

Dongha Lee, Jiaming Shen, SeongKu Kang +3

Topic taxonomies, which represent the latent topic (or category) structure of document collections, provide valuable knowledge of contents in many applications such as web search a…

cs.CL20211 cited

Fine-Grained Opinion Summarization with Minimal Supervision

Suyu Ge, Jiaxin Huang, Yu Meng +2

Opinion summarization aims to profile a target by extracting opinions from multiple documents. Most existing work approaches the task in a semi-supervised manner due to the difficu…

cs.CL202112 cited

Entity Linking Meets Deep Learning: Techniques and Solutions

Wei Shen, Yuhan Li, Yinan Liu +3

Entity linking (EL) is the process of linking entity mentions appearing in web text with their corresponding entities in a knowledge base. EL plays an important role in the fields…

cs.LG202135 cited

Chemical-Reaction-Aware Molecule Representation Learning

Hongwei Wang, Weijiang Li, Xiaomeng Jin +4

Molecule representation learning (MRL) methods aim to embed molecules into a real vector space. However, existing SMILES-based (Simplified Molecular-Input Line-Entry System) or GNN…

cs.CL20211 cited

Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training

Yu Meng, Yunyi Zhang, Jiaxin Huang +4

We study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw…

cs.LG202122 cited

Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data

Qi Zhu, Natalia Ponomareva, Jiawei Han +1

There has been a recent surge of interest in designing Graph Neural Networks (GNNs) for semi-supervised learning tasks. Unfortunately this work has assumed that the nodes labeled f…