基于GPU的常识范式推理的实时查询分类和多模态分析
GPU-based Commonsense Paradigms Reasoning for Real-Time Query Answering and Multimodal Analysis
摘要：
我们利用常识知识库来解决实时的形态多峰分析问题.特别地，我们关注多模态情感分析的问题，其包括不同模态的同时分析，例如，语音和视频，用于情感和极性检测。我们的方法利用现代GPU的大规模并行处理能力来提高不同模态的特征提取的性能。此外，为了从多模态源中提取重要的文本特征，我们基于常识知识生成特定于领域的图，并应用基于GPU的图遍历进行快速特征检测。然后，强大的ELM分类器被应用于建立基于所提取的特征的情感分析模型。我们在YouTube数据集上进行了实验，并达到了78%的准确率，优于所有以前的系统。在处理速度方面，我们的方法显示了几个数量级的改进相比，基于CPU的同行的特征提取。
英文摘要：
We utilize commonsense knowledge bases to address the problem of real- time multimodal analysis. In particular, we focus on the problem of multimodal sentiment analysis, which consists in the simultaneous analysis of different modali- ties, e.g., speech and video, for emotion and polarity detection. Our approach takes advantages of the massively parallel processing power of modern GPUs to enhance the performance of feature extraction from different modalities. In addition, in order to ex- tract important textual features from multimodal sources we generate domain-specific graphs based on commonsense knowledge and apply GPU-based graph traversal for fast feature detection. Then, powerful ELM classifiers are applied to build the senti- ment analysis model based on the extracted features. We conduct our experiments on the YouTube dataset and achieve an accuracy of 78% which outperforms all previous systems. In term of processing speed, our method shows improvements of several orders of magnitude for feature extraction compared to CPU-based counterparts.
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