论文标题
用于文本情感分类的混合瓷砖卷积神经网络
Hybrid Tiled Convolutional Neural Networks for Text Sentiment Classification
论文作者
论文摘要
瓷砖卷积神经网络（瓷砖CNN）仅应用于计算机愿景的学习不向导。我们将其体系结构调整为NLP，以提高最显着特征以进行情感分析。知道NLP字段中瓷砖CNN的主要缺点是其僵化的滤波器结构，我们提出了一种名为Hybrid Tiled CNN的新型架构，该结构仅在相似上下文中出现的单词和邻居词（预防某些N-gram丢失的必要步骤）上应用过滤器）。 IMDB电影评论和Semeval 2017数据集上的实验证明了混合动力瓷砖CNN的效率，其性能比CNN和CNN和瓷砖CNN更好。
The tiled convolutional neural network (tiled CNN) has been applied only to computer vision for learning invariances. We adjust its architecture to NLP to improve the extraction of the most salient features for sentiment analysis. Knowing that the major drawback of the tiled CNN in the NLP field is its inflexible filter structure, we propose a novel architecture called hybrid tiled CNN that applies a filter only on the words that appear in the similar contexts and on their neighbor words (a necessary step for preventing the loss of some n-grams). The experiments on the datasets of IMDB movie reviews and SemEval 2017 demonstrate the efficiency of the hybrid tiled CNN that performs better than both CNN and tiled CNN.
