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Multi-label text classification based on the label correlation mixture model

机译：基于标签相关混合模型的多标签文本分类

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摘要

In the current paper, we propose a probabilistic generative model, the label correlation mixture model (LCMM), to depict multi-labeled document data, which can be utilized for multi-label text classification. LCMM assumes two stochastic generative processes, which correspond to two submodels: 1) a label correlation model; and 2) a label mixture model. The former model formulates labels' generative process, in which a label correlation network is created to depict the dependency between labels. Moreover, we present an efficient inference algorithm for calculating the generative probability of a multi-label class. Furthermore, in order to optimize the label correlation network, we propose a parameter-learning algorithm based on gradient descent. The second submodel in the LCMM depicts the generative process of words in a document with the given labels. Different traditional mixture models can be adopted in this generative process, such as the mixture of language models, or topic models. In the multi-label classification stage, we propose a two-step strategy to most efficiently utilize the LCMM based on the framework of Bayes decision theory. We conduct extensive multi-label classification experiments on three standard text data sets. The experimental results show significant performance improvements comparing to existing approaches. For example, the improvements on accuracy and macro F-score measures in the OHSUMED data set achieve 28.3% and 37.0%, respectively. These performance enhancements demonstrate the effectiveness of the proposed models and solutions.
机译：在当前的论文中，我们提出了一种概率生成模型，即标签相关混合模型（LCMM），用于描述多标签文档数据，该数据可用于多标签文本分类。 LCMM假设有两个随机生成过程，分别对应两个子模型：1）标签相关模型； 2）标签混合物模型。前一个模型制定了标签的生成过程，其中创建了标签相关网络来描述标签之间的依赖性。此外，我们提出了一种有效的推理算法，用于计算多标签类的生成概率。此外，为了优化标签相关网络，提出了一种基于梯度下降的参数学习算法。 LCMM中的第二个子模型描述了带有给定标签的文档中单词的生成过程。在此生成过程中，可以采用不同的传统混合模型，例如语言模型或主题模型的混合。在多标签分类阶段，我们基于贝叶斯决策理论的框架，提出了两步策略以最有效地利用LCMM。我们对三个标准文本数据集进行了广泛的多标签分类实验。实验结果表明，与现有方法相比，性能有了显着提高。例如，OHSUMED数据集中的准确性和宏F分数度量的改进分别达到28.3％和37.0％。这些性能增强证明了所提出的模型和解决方案的有效性。

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