Huan Gui

AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks

2018 · arXiv (Cornell University)

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 to capture the semantic subtlety, we propose the …

Empower Sequence Labeling with Task-Aware Neural Language Model

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Linguistic sequence labeling is a general approach encompassing a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in neural networks (NNs) make it possible to build reliable models without handcrafted …

Empower Sequence Labeling with Task-Aware Neural Language Model

2017 · arXiv (Cornell University)

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 neural networks (NNs) make it possible to build reliable models …