activity
20172019
most citedHeterogeneous Supervision for Relation Extraction: A Representation Learning Approach

19 citations · 23 across the 2 of their papers we have counts for

collaborators

7 papers

cs.CL2019

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…

cs.CL2019

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…

cs.CL2019

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…

cs.IR2018

Expert Finding in Heterogeneous Bibliographic Networks with Locally-trained Embeddings

Huan Gui, Qi Zhu, Liyuan Liu +2

Expert finding is an important task in both industry and academia. It is challenging to rank candidates with appropriate expertise for various queries. In addition, different types…

cs.SI2018

AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks

Yu Shi, Huan Gui, Qi Zhu +2

Heterogeneous information networks (HINs) are ubiquitous in real-world applications. Due to the heterogeneity in HINs, the typed edges may not fully align with each other. In order…

cs.IR20174 cited

Wikidata Vandalism Detection - The Loganberry Vandalism Detector at WSDM Cup 2017

Qi Zhu, Hongwei Ng, Liyuan Liu +4

Wikidata is the new, large-scale knowledge base of the Wikimedia Foundation. As it can be edited by anyone, entries frequently get vandalized, leading to the possibility that it mi…