论文标题
你能告诉吗？ SSNET-矢状层启发的神经网络框架用于情感分析
Can you tell? SSNet -- a Sagittal Stratum-inspired Neural Network Framework for Sentiment Analysis
论文作者
论文摘要
当人们试图理解细微的语言时，他们通常会处理多个输入传感器模式以完成这一认知任务。事实证明，人脑甚至具有专门的神经元形成，称为矢状层，以帮助我们理解讽刺。我们将这种生物形成作为设计神经网络体系结构的灵感，该神经网络结构结合了相同文本上不同模型的预测，以构建强大，准确和计算高效的分类器进行情感分析并研究几种不同的实现。其中，我们提出了一种系统的新方法，以基于专用的神经网络组合多个预测，并对IT进行数学分析以及最新的实验结果进行数学分析。我们还提出了一种启发式杂交技术，用于将模型结合起来，并将其与代表性基准数据集的实验结果进行备份，并与其他方法进行比较，以显示新方法的优势。
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.
