Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2307.16265 v1 pith:SCPZIE4O submitted 2023-07-30 cs.CL cs.AI

Rundong Liu , Wenhan Liang , Weijun Luo , Yuxiang Song , He Zhang , Ruohua Xu , Yunfeng Li , Ming Liu This is my paper
classification cs.CLcs.AI
keywords hierarchicaltextclassificationmulti-labellabelsrecentsurveyadvances
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Platform for Investigating Public Health Content with Efficient Concern Classification

    cs.CL 2025-06 conditional novelty 4.0 of 10

    The paper introduces ConcernScope, a teacher-student platform where GPT-4 labels training data and a BERT model classifies texts into VaxConcerns categories, with a pilot trend analysis on 186,000 passages.

Pith tools