Date Published: June 2020
Author(s)
Apostol Vassilev (NIST), Munawar Hasan (NIST)
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 a robust, accurate and computationally efficient classifier for sentiment analysis. Experimental results on representative benchmark datasets and comparisons to other methods show the advantages of the new network architecture.
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...
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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 a robust, accurate and computationally efficient classifier for sentiment analysis. Experimental results on representative benchmark datasets and comparisons to other methods show the advantages of the new network architecture.
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Keywords
natural language processing; machine learning; deep learning; artificial intelligence
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