[PDF] Top 20 Learning Hierarchical Multi Category Text Classification Models
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Learning Hierarchical Multi Category Text Classification Models
... to category music, it is very likely that the article belongs to category ...in learning the classification have been proposed by several authors (Koller & Sahami, 1997; McCallum et ...new ... See full document
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NeuralClassifier: An Open source Neural Hierarchical Multi label Text Classification Toolkit
... neural hierarchical multi-label text ...neural models for hierarchical multi-label classification task, which is more challenging and common in real-world ...of ... See full document
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Leveraging Hierarchical Category Knowledge for Data Imbalanced Multi Label Diagnostic Text Understanding
... the hierarchical learning with aver- age ...that category knowledge provides informa- tive cues for sharing parameters across low-level codes under the same ...with multi-task learn- ing. The ... See full document
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Adversarial Multi task Learning for Text Classification
... network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant ...adversarial ... See full document
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Multi Dimensional Text Classification
... affect classification accuracy of multi- dimensional category model: training set size and the granularity of ...flat-based classification in the multi-dimensional model deals with the ... See full document
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Extension of TSVM to Multi Class and Hierarchical Text Classification Problems With General Losses
... binary classification problems this algorithm is an improved version of the multiple switching algorithm developed by Sindhwani and Keerthi (2006) for ...of multi-class and hierarchical ... See full document
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Deep Multi Task Learning with Shared Memory for Text Classification
... 5.5 Multi-task Learning of Product Reviews Table 4 shows the classification accuracies on the tasks of product ...our models achieve a better perfor- ...our models can not only share ... See full document
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Weakly-Supervised Hierarchical Text Classification
... the category-aware topics from unlabeled documents for ...probabilistic models. Predictive text embedding (Tang, Qu, and Mei 2015) utilizes both labeled and unlabeled documents to learn text ... See full document
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Hierarchical Text Classification with Reinforced Label Assignment
... flat models. Hierarchical-SVM (Cai and Hofmann, 2004; Qiu et ...(SVM) learning based on discrimi- nant functions that are structured in a way that mirrors the label ...that Hierarchical-SVM ... See full document
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Hierarchical Convolutional Attention Networks for Text Classification
... Text classification is an important research area in natural language processing ...Traditional text classification approaches utilize features gen- erated from vector space models such ... See full document
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Kernel Based Learning of Hierarchical Multilabel Classification Models
... for hierarchical text classification where the documents are allowed to belong to more than one category at a ...The classification model is a variant of the Maximum Margin Markov ... See full document
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Hierarchical Multi label Classification of Text with Capsule Networks
... on multi-labeled documents of the Reuters-21578 dataset since the routing of capsules behaves like a parallel attention mechanism regarding the selec- tion of ...For multi-task learning, Xiao et ... See full document
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Hierarchical deep neural networks for MeSH subject prediction
... Supervised classification problems in machine learning involve identifying the ‘class’ of a particular item (such as an image, a document, an audio or video clip, ...Extreme Multi-Label ... See full document
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Hierarchical Transfer Learning for Multi label Text Classification
... Transfer Learning) is based on a recursive strategy of training parent and child category ...top-level category with C1 as one of its ...transfer learning (Howard and Ruder, 2018) suggested to ... See full document
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Global Model for Hierarchical Multi Label Text Classification
... We further investigated the models by decom- posing them into edges. Figure 2 compares three models. The first three figures (a–c) report the number of non-trivial elements in each weight vector. Edges are ... See full document
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Hierarchical Attention Prototypical Networks for Few Shot Text Classification
... Few-Shot Learning (FSL) aims to solve classifi- cation problems by training a classifier with few instances in each class, and it can apply to un- seen ...transfer learning approaches, Caruana (1994) and ... See full document
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Unified Framework For Deep Learning Based Text Classification
... Deep learning has emerged as a very popular approach for solving large scale pattern recognition ...various text mining problems with improved accuracy as compared to pre-existing ...deep learning ... See full document
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Tangible user interfaces for learning
... The Learning Cube, TagTiles and Towards Utopia express a noun metaphor because the shape of the tangible objects in the physical world is analogous to their digital representation, but the action performed with ... See full document
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Hierarchical Reinforcement Learning for Adaptive Text Generation
... following learning param- eters: the step-size parameter α was initiated with 1 and then reduced over time by α = 1+t 1 , t being the time ...in models M 0 1 and M 0 2 - M 7 2 ...its text strategy to ... See full document
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Hierarchical Classification Based on Label Distribution Learning
... of the class hierarchy should not be penalized in the same way. Thus, we prefer to believe that the hierarchical classifi- cation metrics can evaluate the models better, and the above results can prove the ... See full document
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