Can You Tell? SSNet - A Biologically-Inspired Neural Network Framework for Sentiment Classifiers

@inproceedings{Vassilev2021CanYT,
  title={Can You Tell? SSNet - A Biologically-Inspired Neural Network Framework for Sentiment Classifiers},
  author={Apostol T. Vassilev and Munawar Hasan and Honglan Jin},
  booktitle={LOD},
  year={2021}
}
When people try to understand nuanced language they typically process multiple input sensor modalities to complete this cognitive task. It turns out the human brain has even a specialized neuron formation, called sagittal stratum, to help us understand sarcasm. We use this biological formation as the inspiration for designing a neural network architecture that combines predictions of different models on the same text to construct robust, accurate and computationally efficient classifiers for… 
View PDF on arXiv

References

SHOWING 1-10 OF 47 REFERENCES
GloVe: Global Vectors for Word Representation
TLDR
A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Combining classifiers
We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound…
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
TLDR
A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
‘S’
  • P. Alam
  • Composites Engineering: An A–Z Guide
  • 2021
Deep Learning Based Text Classification: A Comprehensive Review
TLDR
A comprehensive review of more than 150 deep learning based models for text classification developed in recent years are provided, and their technical contributions, similarities, and strengths are discussed.
Underspecification Presents Challenges for Credibility in Modern Machine Learning
TLDR
This work shows the need to explicitly account for underspecification in modeling pipelines that are intended for real-world deployment in any domain, and shows that this problem appears in a wide variety of practical ML pipelines.
Deep Learning--based Text Classification
TLDR
A comprehensive review of more than 150 deep learning--based models for text classification developed in recent years is provided, and their technical contributions, similarities, and strengths are discussed.
A multi-context CNN ensemble for small lesion detection
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
TLDR
Backpropagation through the ODE solver allows each layer to adapt its internal time-step, enabling the network to learn task-relevant time-scales and exceed state-of-the-art performance among RNNs on permuted sequential MNIST.
Microsurgical anatomy of the sagittal stratum
TLDR
The white matter fiber dissection reveals the tridimensional architecture of the SS and the relationship between its fibers, essential to decrease the operative risks when a surgical approach within this area is undertaken.
...
...