Research on patent classification based on hierarchical label semantics

Chen, Jie; Zhao, Shu; Liu, Fan; Liu, Feng; Zhang, Yangping

doi:10.1109/icekim55072.2022.00223

Cited by 1 publication

(1 citation statement)

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“…Several methods apply GCNs to the hierarchy of labels, often in a joint manner with the encoding of the text to enrich the representation [124]. Additional techniques are often applied to improve label correlation and to better model their cooccurrence [125][126][127][128]. Some authors, such as Ning et al [129], argue that graph-based approaches may lose on the directed nature of the tree hierarchy and thus introduce unidirectional message-passing constraints to improve graph embedding.…”

Section: Overview Of Approachesmentioning

confidence: 99%

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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