# LSHTC: A Benchmark for Large-Scale Text Classification

arXiv: https://arxiv.org/abs/1503.08581 · HTML: https://mlsanity.com/paper/1503.08581/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2015.

## Built on by (most cited first, 22 of 22)

- [MATCH: Metadata-Aware Text Classification in A Large Hierarchy](https://mlsanity.com/paper/2102.07349/) (2021)
- [Creating Training Sets via Weak Indirect Supervision](https://mlsanity.com/paper/2110.03484/) (2021)
- [Cluster & Tune: Boost Cold Start Performance in Text Classification](https://mlsanity.com/paper/2203.10581/) (2022)
- [Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning](https://mlsanity.com/paper/2104.01666/) (2021)
- [MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples](https://mlsanity.com/paper/2210.01111/) (2022)
- [Flattening the Parent Bias: Hierarchical Semantic Segmentation in the Poincar'e Ball](https://mlsanity.com/paper/2404.03778/) (2024)
- [Label Disentanglement in Partition-based Extreme Multilabel Classification](https://mlsanity.com/paper/2106.12751/) (2021)
- [A Survey on Extreme Multi-label Learning](https://mlsanity.com/paper/2210.03968/) (2022)
- [Unbiased Loss Functions for Multilabel Classification with Missing Labels](https://mlsanity.com/paper/2109.11282/) (2021)
- [Geometry-Aware Adaptation for Pretrained Models](https://mlsanity.com/paper/2307.12226/) (2023)
- [Multi-label Ranking: Mining Multi-label and Label Ranking Data](https://mlsanity.com/paper/2101.00583/) (2021)
- [Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization](https://mlsanity.com/paper/2109.13122/) (2021)
- [Learning label-label correlations in Extreme Multi-label Classification via Label Features](https://mlsanity.com/paper/2405.04545/) (2024)
- [Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification](https://mlsanity.com/paper/2106.00730/) (2021)
- [Adversarial Examples for Extreme Multilabel Text Classification](https://mlsanity.com/paper/2112.07512/) (2021)
- [The Role of Codeword-to-Class Assignments in Error-Correcting Codes: An Empirical Study](https://mlsanity.com/paper/2302.05334/) (2023)
- [Labels in Extremes: How Well Calibrated are Extreme Multi-label Classifiers?](https://mlsanity.com/paper/2411.04276/) (2024)
- [Federated Learning with Only Positive Labels by Exploring Label Correlations](https://mlsanity.com/paper/2404.15598/) (2024)
- [Multi-label Learning with Random Circular Vectors](https://mlsanity.com/paper/2407.05656/) (2024)
- [Private Multi-Winner Voting for Machine Learning](https://mlsanity.com/paper/2211.15410/) (2022)
- [Text Classification in the Wild: a Large-scale Long-tailed Name Normalization Dataset](https://mlsanity.com/paper/2302.09509/) (2023)
- [Label Embedding via Low-Coherence Matrices](https://mlsanity.com/paper/2305.19470/) (2023)

## 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.