A collection of nine multi-label text classification datasets

This is a compressed package containing nine multi-label text classification data sets, including AAPD, CitySearch, Heritage, Laptop, Ohsumed, RCV1, Restaurant, Reuters, and Sentihood. The datasets of CitySearch, Heritage, Laptop, Restaurant and Sentihood are from the paper of “Bert-flow-vae: A weakly- supervised model for multi-label text classification” (url: https://aclanthology.org/2022.coling-1.104/). The original datasets of Reuters and Ohsumed are from http://disi.unitn.it/moschitti/corpora.htm. The original dataset of AAPD is from https://github.com/lancopku/SGM. The original dataset of RCV1 is from http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/lyrl2004_rcv1v2_README.htm. For all of these datasets, we adopt the raw text format. For Reuters, we retain the 10 largest classes. In terms of Reuters and Ohsumed, their category words are directly obtained from the descriptive words and seed words defined in [1] and [2]. Interms of the other datasets, we generate their category words with the protocol described in our proposed category word selection method CWS-SRC. [1] X. Chen, Y. Xia, P. Jin, and J. Carroll, “Dataless text classification with descriptive lda,” in AAAI, 2015, pp. 2224–2231.[2] D. Zha and C. Li, “Multi-label dataless text classification with topic modeling,” KAIS, vol. 61, no. 1, pp. 137–160, 2019

搜集汇总

背景与挑战

背景概述

该数据集是一个包含九个多标签文本分类数据集的集合，具体包括AAPD、CitySearch、Heritage、Laptop、Ohsumed、RCV1、Restaurant、Reuters和Sentihood。数据以原始文本格式提供，每个子数据集包含训练和测试样本的文本、标签以及自生成的标签名称，适用于人工智能和机器学习领域的多标签分类研究。