19 citations · 57 across the 10 of their papers we have counts for
6 papers · 1 filter
Patton: Language Model Pretraining on Text-Rich Networks
Bowen Jin, Wentao Zhang, Yu Zhang +4
A real-world text corpus sometimes comprises not only text documents but also semantic links between them (e.g., academic papers in a bibliographic network are linked by citations…
Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks
Bowen Jin, Yu Zhang, Qi Zhu +1
Representation learning on networks aims to derive a meaningful vector representation for each node, thereby facilitating downstream tasks such as link prediction, node classificat…
Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity
Yu Shi, Jiaming Shen, Yuchen Li +7
Text-rich heterogeneous information networks (text-rich HINs) are ubiquitous in real-world applications. Hypernymy, also known as is-a relation or subclass-of relation, lays in the…
Facet-Aware Evaluation for Extractive Summarization
Yuning Mao, Liyuan Liu, Qi Zhu +2
Commonly adopted metrics for extractive summarization focus on lexical overlap at the token level. In this paper, we present a facet-aware evaluation setup for better assessment of…
Task-Guided Pair Embedding in Heterogeneous Network
Chanyoung Park, Donghyun Kim, Qi Zhu +2
Many real-world tasks solved by heterogeneous network embedding methods can be cast as modeling the likelihood of pairwise relationship between two nodes. For example, the goal of…
Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach
Liyuan Liu, Xiang Ren, Qi Zhu +4
Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-co…