你看得出来吗？SSNet --一个基于STEM的情感分析神经网络框架
Can you tell? SSNet -- a Sagittal Stratum-inspired Neural Network Framework for Sentiment Analysis
摘要：
当人们试图理解细微差别的语言时，他们通常会处理多个输入传感器模态来完成这个认知任务。事实证明，人类大脑甚至有一个专门的神经元结构，称为矢状层，以帮助我们理解讽刺。我们使用这种生物形成的灵感，设计一个神经网络架构，结合不同的模型对同一文本的预测，构建强大的，准确的和计算效率高的分类情感分析和研究几种不同的实现。其中，我们提出了一种系统的新方法，结合基于专用神经网络的多个预测，并开发它的数学分析以及最先进的实验结果。我们还提出了一种混合技术，用于组合模型，并在一个代表性的基准数据集上用实验结果进行备份，并与其他方法进行比较，以显示新方法的优势。
英文摘要：
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 sentiment analysis and study several different realizations. Among them, we propose a systematic new approach to combining multiple predictions based on a dedicated neural network and develop mathematical analysis of it along with state-of-the-art experimental results. We also propose a heuristic-hybrid technique for combining models and back it up with experimental results on a representative benchmark dataset and comparisons to other methods to show the advantages of the new approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
