[PDF] Top 20 Hierarchical Convolutional Attention Networks for Text Classification
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Hierarchical Convolutional Attention Networks for Text Classification
... for text processing can in- herently account for word order when extracting ...of convolutional multihead self-attention do not have this ...the convolutional multihead ... See full document
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Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data
... and text data using convolutional attention ...proposed attention model performs much better in classifying emotion from speech and text data con- tained in the CMU-MOSEI ... See full document
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Brain Tumor Classification Using Convolutional Neural Networks
... tumor classification with high accuracy, performance and low ...tumor classification is performed by using Fuzzy C Means (FCM) based segmentation, texture and shape feature extraction and SVM and DNN based ... See full document
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Khmer handwritten text recognition with Convolution Neural Networks
... different convolutional networks have been trained and their performance has been ...based classification based on full feature set, as well as classification based on FS with Gabor filters, ... See full document
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Sequential Short Text Classification with Recurrent and Convolutional Neural Networks
... short- text representation level and the class representa- tion level does not help in most cases and may even lower the ...short- text representations contain richer and more gen- eral information than ... See full document
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Cancer Hallmark Text Classification Using Convolutional Neural Networks
... We considered a range of modifications to the basic CNN model to better adapt it to biomedical domain text classification in general and the specific task studied in this work in particular. Of these ... See full document
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Initializing neural networks for hierarchical multi label text classification
... input text, where the la- bels are a part of a hierarchical structure (such as a ...(OVR) classification setup, where a binary clas- sifier is trained for each label in the tax- onomy or ontology ... See full document
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Hierarchical Multi label Classification of Text with Capsule Networks
... Capsule Networks: Capsule networks encap- sulate features into groups of neurons, so-called capsules (Hinton et ...age classification task where each digit has been associated with a capsule, ... See full document
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HIERARCHICAL TEXT CLASSIFICATION USING DICTIONARY BASED APPROACH AND CONVOLUTIONAL NEURAL NETWORK
... science. Text categorization is part of a wide range of machine learning ...This classification problem can be solved by various ...and convolutional neural networks were used to classify ... See full document
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Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health
... neural networks (CNNs) applied to this task have shown good performance in previous stud- ies (Gkotsis et ...neural networks (RNNs) for the same task remains ... See full document
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Keeping Consistency of Sentence Generation and Document Classification with Multi Task Learning
... between attention weights occasionally disturbs the model to gen- erate proper ...input text than the key phrase ...the attention weights among tasks generally maintain a hierarchical ... See full document
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Weakly-Supervised Hierarchical Text Classification
... global hierarchical loss functions that regularize with the ...for hierarchical text classification rely on traditional text clas- ...neural networks have demonstrated superior ... See full document
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Clinical text classification with rule-based features and knowledge-guided convolutional neural networks
... clinical text classification rely on biomedical knowledge sources ...narrative text to concepts from knowledge sources like Unified Medical Language System (UMLS), then train classifiers on document ... See full document
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Heterogeneous Graph Attention Networks for Semi supervised Short Text Classification
... neural networks which automatically rep- resent texts as embeddings, have been widely used for text ...including text classification. To adapt it to short text classification, several meth- ods ... See full document
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Hierarchical Attention Networks for Sentence Ordering
... Two typical tasks are commonly used to build and evalu- ate models that understand coherence. One is a discrimina- tion task that identifies the more coherent ordering given a document and a permuted version of it. ... See full document
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Very Deep Convolutional Networks for Text Classification
... Each convolutional block (see Figure 2) is a se- quence of two convolutional layers, each one followed by a temporal BatchNorm (Ioffe and Szegedy, 2015) layer and an ReLU ...more convolutional layers ... See full document
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Hierarchical Classification with Convolutional Neural Networks for Biomedical Literature
... speaking, hierarchical classification has two ...the hierarchical structure, some researchers have imposed statistical similarity constraints between the probabilistic models for adjacent vertices in ... See full document
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Hierarchical Attention Networks for Document Classification
... Figure 5 shows that our model can select the words carrying strong sentiment like delicious, amazing, terrible and their corresponding sentences. Sentences containing many words like cocktails, pasta, entree are disre- ... See full document
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Graph Convolutional Networks for Text Classification
... why Text GCN works well are two fold: 1) the text graph can capture both document-word relations and global word-word relations; 2) the GCN model, as a spe- cial form of Laplacian smoothing, computes the ... See full document
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Hierarchical Attention Prototypical Networks for Few Shot Text Classification
... level attention, we highlight the im- portance of the word “daughter”, which appears in the query instance and the first support instance of class “mother” at the same time, then this support instance get the ... See full document
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