34 Deep Learning For Extreme Multi Label Text Classification

Extreme multi label text classification xmtc refers to the problem of assigning to each document its most relevant subset of class labels from an extremely large label collection where the number of labels could reach hundreds of thousands or millions. In this paper we establish a three stage framework to solve xmc efficiently which includes 1 indexing the.

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Most existing deep models for multi label text classification consider either non consecutive and long distance semantics or sequential semantics.

Deep learning for extreme multi label text classification. Due to modern applications that lead to massive label sets the scalability of xmc has attracted much recent attention from both academia and industry. The huge label space raises research challenges such as data sparsity and scalability. Specifically we build the model consisting of attention mechanism convolutional neural network and recurrent neural network to extract multi view features.

In order to improve the performance of deep learning method for extreme multi label text classification we propose a novel feature extraction method to better explore the text space. Extreme multi label classification xmc aims to assign to an instance the most relevant subset of labels from a colossal label set. Many applications have been found in.

We extend traditional deep learning framework on multi label classification problem by establishing non linear embedding in both feature and label spaces. Extreme multi label text classication xmtc refers to the prob lem of assigning to each document its most relevant subset of class labels from an extremely large label collection where the number of labels could reach hundreds of thousands or millions. Extreme classification is a rapidly growing research area focusing on multi class and multi label problems involving an extremely large number of labels.

Extreme multi label text classification xmtc refers to the problem of assigning to each document its most relevant subset of class labels from an extremely large label collection where the number of labels could reach hundreds of thousands or millions. To our best knowledge our method is devotes an initial attempt to model the feature space non linearity and label graph structure simultane ously for the xml problem. Extreme multi label classification xmc aims to assign to an instance the most relevant subset of labels from a colossal label set.

The huge label space raises research challenges such as data sparsity and scalability. Due to modern applications that lead to massive label sets the scalability of xmc has attracted much recent attention from both academia and industry.

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