Hierarchical multi-label text classification aims to classify the input text
into multiple labels, among which the labels are structured and hierarchical.
It is a vital task in many real world applications, e.g. scientific literature
archiving. In this paper, we survey the recent progress of hierarchical
multi-label text classification, including the open sourced data sets, the main
methods, evaluation metrics, learning strategies and the current challenges. A
few future research directions are also listed for community to further improve
this field.

本文综述了层次化多标签文本分类的最新进展，包括开源数据集、主要方法、评估指标、学习策略和当前面临的挑战，并列举了一些未来的研究方向，以便社区进一步改进该领域。

层次多标签文本分类的最新进展：一项综述

Recent Advances in Hierarchical Multi-label Text Classification: A  Survey

We propose a study of structured non-convex non-concave min-max problems
which goes beyond standard first-order approaches. Inspired by the tight
understanding established in recent works [Adil et al., 2022, Lin and Jordan,
2022b], we develop a suite of higher-order methods which show the improvements
attainable beyond the monotone and Minty condition settings. Specifically, we
provide a new understanding of the use of discrete-time $p^{th}$-order methods
for operator norm minimization in the min-max setting, establishing an
$O(1/\epsilon^\frac{2}{p})$ rate to achieve $\epsilon$-approximate
stationarity, under the weakened Minty variational inequality condition of
Diakonikolas et al. [2021]. We further present a continuous-time analysis
alongside rates which match those for the discrete-time setting, and our
empirical results highlight the practical benefits of our approach over
first-order methods.

使用高阶方法结合离散 / 连续时间分析，研究了结构化的非凸非凹 min-max 问题中算子范数最小化问题的近似最优解，与一阶方法相比，实验结果表明其具有显著优势。