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.
翻译:当人们试图理解细微语言时, 他们通常会处理多种输入传感器模式来完成认知任务。 事实证明, 人类大脑甚至有一个专门的神经结构, 叫做 人形层, 来帮助我们理解讽刺。 我们用这种生物形成来启发我们设计一个神经网络结构, 将同一文本上不同模型的预测结合起来, 以构建强大、 准确和计算高效的分类器, 用于感应分析并研究多种不同的实现。 其中, 我们提出一种系统的新办法, 将基于专用神经网络的多重预测合并起来, 并发展数学分析, 以及最新实验结果。 我们还提出一种超自然的混合技术, 将模型与具有代表性的基准数据集的实验结果相配合, 并与其他方法进行比较, 以显示新方法的优势 。