64 citations · 70 across the 7 of their papers we have counts for
7 papers
Hypergraph Pattern Machine: Compositional Tokenization for Higher-Order Interactions
Kyrie Zhao, Zehong Wang, Tianyi Ma +5
Hypergraphs model higher-order relations that drive real-world decisions, from drug prescriptions to recommendations. A central structural signal in such data, beyond what pairwise…
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Han Xie, Da Zheng, Jun Ma +9
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain. In the graph mining domain, a similar analogy can be…
Retrieval augmentation of large language models for lay language generation
Yue Guo, Wei Qiu, Gondy Leroy +2
Recent lay language generation systems have used Transformer models trained on a parallel corpus to increase health information accessibility. However, the applicability of these m…
Graph-in-Graph Network for Automatic Gene Ontology Description Generation
Fenglin Liu, Bang Yang, Chenyu You +4
Gene Ontology (GO) is the primary gene function knowledge base that enables computational tasks in biomedicine. The basic element of GO is a term, which includes a set of genes wit…
GraphPrompt: Graph-Based Prompt Templates for Biomedical Synonym Prediction
Hanwen Xu, Jiayou Zhang, Zhirui Wang +5
In the expansion of biomedical dataset, the same category may be labeled with different terms, thus being tedious and onerous to curate these terms. Therefore, automatically mappin…
Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation
Fenglin Liu, Chenyu You, Xian Wu +3
Medical report generation, which aims to automatically generate a long and coherent report of a given medical image, has been receiving growing research interests. Existing approac…
Graphine: A Dataset for Graph-aware Terminology Definition Generation
Zequn Liu, Shukai Wang, Yiyang Gu +3
Precisely defining the terminology is the first step in scientific communication. Developing neural text generation models for definition generation can circumvent the labor-intens…