Gated Neural Network with Regularized Loss for Multi-label Text Classification

Xu, Yunlai; Ran, Xiangying; Sun, Wei; Luo, Xiangyang; Wang, Chongjun

doi:10.1109/ijcnn.2019.8851686

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…SGM (Yang et al, 2018b) applies the seq2seq model with attention mechanism, which takes the global contextual information. REGGNN (Xu et al, 2019) uses a combination of CNN and LSTM with a dynamic gate that controls the information from these two parts. NLP-CAP (Zhao et al, 2019) is a capsule-based approach for MLC, which reformulates the routing algorithm.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…The traditional MLC method SLEEC (Bhatia et al, 2015) makes use of label correlations by embedding the label co-occurrence graph. The seq2seq model SGM (Yang et al, 2018b) uses the attention mechanism to consider the label correlations, while REGGNN (Xu et al, 2019) applies a regularized loss specified for label co-occurrence. REGGNN additionally chooses to dynamically combine the local and global contextual information to construct document representations.…”

Section: Multi-label Classificationmentioning

confidence: 99%

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Hyperbolic Capsule Networks for Multi-Label Classification

Chen¹,

Huang²,

Xiao³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Although deep neural networks are effective at extracting high-level features, classification methods usually encode an input into a vector representation via simple feature aggregation operations (e.g. pooling). Such operations limit the performance. For instance, a multi-label document may contain several concepts. In this case, one vector can not sufficiently capture its salient and discriminative content. Thus, we propose Hyperbolic Capsule Networks (HYPERCAPS) for Multi-Label Classification (MLC), which have two merits. First, hyperbolic capsules are designed to capture fine-grained document information for each label, which has the ability to characterize complicated structures among labels and documents. Second, Hyperbolic Dynamic Routing (HDR) is introduced to aggregate hyperbolic capsules in a label-aware manner, so that the label-level discriminative information can be preserved along the depth of neural networks. To efficiently handle large-scale MLC datasets, we additionally present a new routing method to adaptively adjust the capsule number during routing. Extensive experiments are conducted on four benchmark datasets. Compared with the state-of-the-art methods, HY-PERCAPS significantly improves the performance of MLC especially on tail labels.

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Section: Evaluation Metricsmentioning

confidence: 99%

Section: Multi-label Classificationmentioning

confidence: 99%