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

本压缩包包含9个多标签文本分类（multi-label text classification）数据集，分别为AAPD、CitySearch、Heritage、Laptop、Ohsumed、RCV1、Restaurant、Reuters与Sentihood。其中CitySearch、Heritage、Laptop、Restaurant及Sentihood这5个数据集源自论文《Bert-flow-vae：一种面向多标签文本分类的弱监督（weakly-supervised）模型》（链接：https://aclanthology.org/2022.coling-1.104/）。Reuters与Ohsumed的原始数据集源自http://disi.unitn.it/moschitti/corpora.htm。AAPD的原始数据集源自https://github.com/lancopku/SGM。RCV1的原始数据集源自http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/lyrl2004_rcv1v2_README.htm。针对上述所有数据集，本工作均采用原始文本格式进行处理。针对Reuters数据集，我们保留了其中规模最大的10个类别。就Reuters与Ohsumed数据集而言，其类别词直接取自文献[1]与[2]中定义的描述词与种子词。对于其余数据集，我们则采用本文提出的类别词选取方法CWS-SRC中描述的流程生成其类别词。[1] X. Chen、Y. Xia、P. Jin与J. Carroll，《基于描述性LDA的无数据文本分类（dataless text classification）》，发表于AAAI，2015年，第2224–2231页。[2] D. Zha与C. Li，《基于主题建模的多标签无数据文本分类（multi-label dataless text classification）》，发表于KAIS，第61卷第1期，第137–160页，2019年

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。