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arXiv · 1904.12578

HAXMLNet: Hierarchical Attention Network for Extreme Multi-Label Text Classification

Ronghui You·Zihan Zhang·Suyang Dai·Shanfeng Zhu
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Abstract

Extreme multi-label text classification (XMTC) addresses the problem of tagging each text with the most relevant labels from an extreme-scale label set. Traditional methods use bag-of-words (BOW) representations without context information as their features. The state-ot-the-art deep learning-based method, AttentionXML, which uses a recurrent neural network (RNN) and the multi-label attention, can hardly deal with extreme-scale (hundreds of thousands labels) problem. To address this, we propose our HAXMLNet, which uses an efficient and effective hierarchical structure with the multi-label attention. Experimental results show that HAXMLNet reaches a competitive performance with other state-of-the-art methods.

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Ronghui You, Zihan Zhang, Suyang Dai, Shanfeng Zhu. 2019-03-24. HAXMLNet: Hierarchical Attention Network for Extreme Multi-Label Text Classification. https://arxiv.org/abs/1904.12578

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