.png){kind=icon}
返回
Statistical topic models for multi-label document classification
.png){kind=icon}
OA
AI
DOI：10.1007/s10994-011-5272-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Machine learning approaches to multi-label document classification have to date largely relied on discriminative modeling techniques such as support vector machines. A drawback of these approaches is that performance rapidly drops off as the total number of labels and the number of labels per document increase. This problem is amplified when the label frequencies exhibit the type of highly skewed distributions that are often observed in real-world datasets. In this paper we investigate a class of generative statistical topic models for multi-label documents that associate individual word tokens with different labels. We investigate the advantages of this approach relative to discriminative models, particularly with respect to classification problems involving large numbers of relatively rare labels. We compare the performance of generative and discriminative approaches on document labeling tasks ranging from datasets with several thousand labels to datasets with tens of labels. The experimental results indicate that probabilistic generative models can achieve competitive multi-label classification performance compared to discriminative methods, and have advantages for datasets with many labels and skewed label frequencies.
Keyword:
Topic models
LDA
Multi-label classification
Document modeling
Text classification
Graphical models
Probabilistic generative models
Dependency-LDA
期刊
IF:
2.9
论文数:
2.7K
被引数:
3.4W
机构
引用论文
分享收藏Sampling efficiency of Amberlite XAD-2 with glassfiber prefilter and activated charcoal as back-up section for collection of mixtures of organic air pollutants at the μg/m3 level.png)
Chemosphere
ChemosphereIF0
PREAI
分享收藏
分享收藏
查看更多内容