most citedEmpower Sequence Labeling with Task-Aware Neural Language Model

151 citations · 186 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2017

Integrating Knowledge from Latent and Explicit Features for Triple Scoring - Team Radicchio's Triple Scorer at WSDM Cup 2017

Liang-Wei Chen, Bhargav Mangipudi, Jayachandu Bandlamudi +4

The objective of the triple scoring task in WSDM Cup 2017 is to compute relevance scores for knowledge-base triples of type-like relations. For example, consider Julius Caesar who…

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…

cs.CL2017151 cited

Empower Sequence Labeling with Task-Aware Neural Language Model

Liyuan Liu, Jingbo Shang, Frank F. Xu +4

Linguistic sequence labeling is a general modeling approach that encompasses a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in…

cs.CL201719 cited

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…

stat.ML201512 cited

Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation

Huan Gui, Quanquan Gu

We present a unified framework for low-rank matrix estimation with nonconvex penalties. We first prove that the proposed estimator attains a faster statistical rate than the tradit…