mlsanityData to 7 Oct 2026

LSHTC: A Benchmark for Large-Scale Text Classification

arXiv 1503.08581 ↗First posted 30 Mar 2015cs.IR cs.CL cs.LGcited by 22

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Abstract (arXiv)

LSHTC is a series of challenges which aims to assess the performance of classification systems in large-scale classification in a a large number of classes (up to hundreds of thousands). This paper describes the dataset that have been released along the LSHTC series. The paper details the construction of the datsets and the design of the tracks as well as the evaluation measures that we implemented and a quick overview of the results. All of these datasets are available online and runs may still be submitted on the online server of the challenges.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Mar 2015.

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Citing papers per year: 2021: 9; 2022: 4; 2023: 4; 2024: 5.

22 papers.

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  1. 2021MATCH: Metadata-Aware Text Classification in A Large Hierarchycited by 20
  2. 2021Creating Training Sets via Weak Indirect Supervisioncited by 14
  3. 2022Cluster & Tune: Boost Cold Start Performance in Text Classificationcited by 13
  4. 2021Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoningcited by 9
  5. 2022MultiGuard: Provably Robust Multi-label Classification against Adversarial Examplescited by 9
  6. 2024Flattening the Parent Bias: Hierarchical Semantic Segmentation in the Poincar'e Ballcited by 9
  7. 2021Label Disentanglement in Partition-based Extreme Multilabel Classificationcited by 7
  8. 2022A Survey on Extreme Multi-label Learningcited by 7
  9. 2021Unbiased Loss Functions for Multilabel Classification with Missing Labelscited by 5
  10. 2023Geometry-Aware Adaptation for Pretrained Modelscited by 5
  11. 2021Multi-label Ranking: Mining Multi-label and Label Ranking Datacited by 3
  12. 2021Speeding-up One-vs-All Training for Extreme Classification via Smart Initializationcited by 3
10 more
  1. 2024Learning label-label correlations in Extreme Multi-label Classification via Label Featurescited by 3
  2. 2021Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classificationcited by 2
  3. 2021Adversarial Examples for Extreme Multilabel Text Classificationcited by 2
  4. 2023The Role of Codeword-to-Class Assignments in Error-Correcting Codes: An Empirical Studycited by 2
  5. 2024Labels in Extremes: How Well Calibrated are Extreme Multi-label Classifiers?cited by 2
  6. 2024Federated Learning with Only Positive Labels by Exploring Label Correlationscited by 1
  7. 2024Multi-label Learning with Random Circular Vectorscited by 1
  8. 2022Private Multi-Winner Voting for Machine Learningcited by 0
  9. 2023Text Classification in the Wild: a Large-scale Long-tailed Name Normalization Datasetcited by 0
  10. 2023Label Embedding via Low-Coherence Matricescited by 0