Hongyu Guo

The Unreasonable Effectiveness of Word Representations for Twitter Named Entity Recognition

2015

Named entity recognition (NER) systems trained on newswire perform very badly when tested on Twitter. Signals that were reliable in copy-edited text disappear almost entirely in Twitter’s informal chatter, requiring the construction of specialized models. …

A Deep Network with Visual Text Composition Behavior

2017 · arXiv (Cornell University)

While natural languages are compositional, how state-of-the-art neural models achieve compositionality is still unclear. We propose a deep network, which not only achieves competitive accuracy for text classification, but also exhibits compositional behavior. That is, …

MixUp as Locally Linear Out-of-Manifold Regularization

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

MixUp (Zhang et al. 2017) is a recently proposed dataaugmentation scheme, which linearly interpolates a random pair of training examples and correspondingly the one-hot representations of their labels. Training deep neural networks with such additional …

Intrusion-Free Graph Mixup

2021 · arXiv (Cornell University)

We present a simple and yet effective interpolation-based regularization technique to improve the generalization of Graph Neural Networks (GNNs). We leverage the recent advances in Mixup regularizer for vision and text, where random sample pairs …

Long Short-Term Memory Over Recursive Structures

2015 · NPARC

The chain-structured long short-term memory (LSTM) has showed to be effective in a wide range of problems such as speech recognition and machine translation. In this paper, we pro-pose to extend it to tree structures, …