HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification

He, Zhonghu; Zhang, Chong; Huang, Junjie; Wu, Junran; Xu, Ke

doi:10.18653/v1/2023.acl-long.432

Cited by 7 publications

(2 citation statements)

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“…The latter can be found in Tables 9 and 10, while the results over the individual splits are reported in Table 11. [149] 0.810 0.533 --HiLAP [99] 0.833 0.601 --HiAGM-TP [97] 0.840 0.634 0.858 0.803 RLHR [167] --0.785 0.792 HCSM [168] 0.858 0.609 0.921 0.807 HiMatch [124] 0.847 0.641 0.862 0.805 HIDDEN [169] 0.793 0.473 --HE-AGCRCNN [170] 0.778 0.513 --HVHMC [171] --0.743 -SASF [126] --0.867 0.811 HTCInfoMax [177] 0.835 0.627 0.856 0.800 PAAM-HiA-T5 [178] 0.872 0.700 0.904 0.816 HPT [136] 0.873 0.695 0.872 0.819 HGCLR [91] 0.865 0.683 0.871 0.812 Seq2Tree [93] 0.869 0.700 0.872 0.825 HBGL [180] 0.872 0.711 0.874 0.820 P-tuning v2 (SPP-tuning) [138] --0.875 0.800 LD-GGNN [186] 0.842 0.641 0.851 0.805 LSE-HiAGM [123] 0.839 0.646 0.860 0.800 Seq2Label [121] 0.874 0.706 0.873 0.819 HTC-CLIP [190] --0.879 0.816 GACaps [191] 0.868 0.698 0.876 0.828 HiDEC [194] 0.855 0.651 --UMP-MG [129] --0.859 0.813 LED [196] 0.883 0.697 0.870 0.813 (HGCLR-based + aug) [198] 0.862 0.679 0.874 0.821 K-HTC [135] --0.873 0.817 HiTIN (BERT) [200] 0.867 0.699 0.872 0.816 HierVerb (few-shot) [137] 0 The standard deviation over the 2 repetitions of 3-fold cross-validation is reported in brackets.…”

Section: Resultsmentioning

confidence: 99%

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Hierarchical Text Classification and Its Foundations: A Review of Current Research

Zangari,

Marcuzzo,

Rizzo

et al. 2024

Electronics

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While collections of documents are often annotated with hierarchically structured concepts, the benefits of these structures are rarely taken into account by classification techniques. Within this context, hierarchical text classification methods are devised to take advantage of the labels’ organization to boost classification performance. In this work, we aim to deliver an updated overview of the current research in this domain. We begin by defining the task and framing it within the broader text classification area, examining important shared concepts such as text representation. Then, we dive into details regarding the specific task, providing a high-level description of its traditional approaches. We then summarize recently proposed methods, highlighting their main contributions. We also provide statistics for the most commonly used datasets and describe the benefits of using evaluation metrics tailored to hierarchical settings. Finally, a selection of recent proposals is benchmarked against non-hierarchical baselines on five public domain-specific datasets. These datasets, along with our code, are made available for future research.

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Section: Resultsmentioning

confidence: 99%

“…Zhu et al [200] propose to convert the label hierarchy into a simplified tree structure to remove noisy (or less discriminative) information and then encode this knowledge in the text representations. First, text is encoded using BERT (though any text encoder would work).…”

Section: Hitinmentioning

confidence: 99%