Sequential Learning of Convolutional Features for Effective Text Classification
Text classification has been one of the major problems in natural language processing. With the advent of deep learning, convolutional neural network (CNN) has been a popular solution to this task. However, CNNs which were first proposed for images, face many crucial challenges in the context of text processing, namely in their elementary blocks: convolution filters and max pooling. These challenges have largely been overlooked by the most existing CNN models proposed for text classification. In this paper, we present an experimental study on the fundamental blocks of CNNs in text categorization. Based on this critique, we propose Sequential Convolutional Attentive Recurrent Network (SCARN). The proposed SCARN model utilizes both the advantages of recurrent and convolutional structures efficiently in comparison to previously proposed recurrent convolutional models. We test our model on different text classification datasets across tasks like sentiment analysis and question classification. Extensive experiments establish that SCARN outperforms other recurrent convolutional architectures with significantly less parameters. Furthermore, SCARN achieves better performance compared to equally large various deep CNN and LSTM architectures.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationSentiment AnalysisText Categorizationtext-classificationText ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification
Graph-based Aspect-based Sentiment Classification (ABSC) approaches have yielded state-of-the-art results, expecially when equipped with contextual word embedding from pre-training language models (PLMs). However, they i…
Sentiment AnalysisSentiment ClassificationConvolutional RNN: an Enhanced Model for Extracting Features from Sequential Data
Traditional convolutional layers extract features from patches of data by applying a non-linearity on an affine function of the input. We propose a model that enhances this feature extraction process for the case of sequ…
Audio ClassificationStable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks
Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. We show that temporal convolutional …
ClassificationGeneral ClassificationTensor Graph Convolutional Networks for Text Classification
Compared to sequential learning models, graph-based neural networks exhibit some excellent properties, such as ability capturing global information. In this paper, we investigate graph-based neural networks for text clas…
ClassificationGeneral Classificationtext-classificationText ClassificationSequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification
A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature d…
ClassificationDimensionality ReductionGeneral Classification