52 citations · 196 across the 15 of their papers we have counts for
7 papers · 1 filter
A Hierarchical Location Prediction Neural Network for Twitter User Geolocation
Binxuan Huang, Kathleen M. Carley
Accurate estimation of user location is important for many online services. Previous neural network based methods largely ignore the hierarchical structure among locations. In this…
Location Order Recovery in Trails with Low Temporal Resolution
Binxuan Huang, Kathleen M. Carley
Researchers who study object movement problems related to topics like traffic flow analysis, patient monitoring, and software operation, need to know the correct order in which obj…
Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification
Binxuan Huang, Kathleen M. Carley
We introduce a novel parameterized convolutional neural network for aspect level sentiment classification. Using parameterized filters and parameterized gates, we incorporate aspec…
Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks
Binxuan Huang, Kathleen M. Carley
Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. Previous neural network based methods largely ignore the…
A Large-Scale Empirical Study of Geotagging Behavior on Twitter
Binxuan Huang, Kathleen M. Carley
Geotagging on social media has become an important proxy for understanding people's mobility and social events. Research that uses geotags to infer public opinions relies on severa…
Recurrent U-net: Deep learning to predict daily summertime ozone in the United States
Tai-Long He, Dylan B. A. Jones, Binxuan Huang +6
We use a hybrid deep learning model to predict June-July-August (JJA) daily maximum 8-h average (MDA8) surface ozone concentrations in the US. A set of meteorological fields from t…
Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning
Binxuan Huang, Kathleen M. Carley
In this paper, we study the problem of node representation learning with graph neural networks. We present a graph neural network class named recurrent graph neural network (RGNN),…