Very Deep Convolutional Networks for Text Classification
The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks. However, these architectures are rather shallow in comparison to the deep convolutional networks which have pushed the state-of-the-art in computer vision. We present a new architecture (VDCNN) for text processing which operates directly at the character level and uses only small convolutions and pooling operations. We are able to show that the performance of this model increases with depth: using up to 29 convolutional layers, we report improvements over the state-of-the-art on several public text classification tasks. To the best of our knowledge, this is the first time that very deep convolutional nets have been applied to text processing.
Code (24)
Tasks
General ClassificationText ClassificationSimilar Papers 제목 키워드 기반
Very high resolution Airborne PolSAR Image Classification using Convolutional Neural Networks
In this work, we exploit convolutional neural networks (CNNs) for the classification of very high resolution (VHR) polarimetric SAR (PolSAR) data. Due to the significant appearance of heterogeneous textures within these …
ClassificationGeneral Classificationimage-classificationImage Classification+2Text classification with pixel embedding
We propose a novel framework to understand the text by converting sentences or articles into video-like 3-dimensional tensors. Each frame, corresponding to a slice of the tensor, is a word image that is rendered by the w…
ArticlesClassificationGeneral ClassificationSentence+2Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification
Geometric 3D scene classification is a very challenging task. Current methodologies extract the geometric information using only a depth channel provided by an RGB-D sensor. These kinds of methodologies introduce possibl…
ClassificationGeneral ClassificationScene ClassificationAn Attention-Gated Convolutional Neural Network for Sentence Classification
The classification of sentences is very challenging, since sentences contain the limited contextual information. In this paper, we proposed an Attention-Gated Convolutional Neural Network (AGCNN) for sentence classificat…
ClassificationGeneral ClassificationSentenceSentence ClassificationReal-World Font Recognition Using Deep Network and Domain Adaptation
We address a challenging fine-grain classification problem: recognizing a font style from an image of text. In this task, it is very easy to generate lots of rendered font examples but very hard to obtain real-world labe…
Domain AdaptationFont RecognitionGeneral Classification