Text classification is the task of assigning a sentence or document an appropriate category. The categories depend on the chosen dataset and can range from topics.
|Trend||Dataset||Best Method||Paper title||Paper||Code||Compare|
In this paper, we represent documents as word co-occurrence networks and propose an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD).
Experiment results show that the developed hULMonA and multi-lingual ULM are able to generalize well to multiple Arabic data sets and achieve new state of the art results in Arabic Sentiment Analysis for some of the tested sets.
Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflict between citizen.
We study the effect of different approaches to text augmentation.
While very deep neural networks have shown effectiveness for computer vision and text classification applications, how to increase the network depth of neural machine translation (NMT) models for better translation quality remains a challenging problem.
Experiments on three popular datasets using convolutional as well as LSTM models show that PWWS reduces the classification accuracy to the most extent, and keeps a very low word substitution rate.
In this work, we present graph star net (GraphStar), a novel and unified graph neural net architecture which utilizes message-passing relay and attention mechanism for multiple prediction tasks - node classification, graph classification and link prediction.
SOTA for Link Prediction on Cora