论文标题
层次多标签文本分类的单词和类别标签的联合嵌入
Joint Embedding of Words and Category Labels for Hierarchical Multi-label Text Classification
论文作者
论文摘要
由于分类标签粒度和分类标签量表的扩展，文本分类变得越来越具有挑战性。为了解决这一问题，已经将一些研究应用于策略，以利用大量类别的问题中的层次结构。目前，等级文本分类（HTC）已受到广泛关注，并具有广泛的应用前景。在文本分类任务中充分利用父级类别和子类别之间的关系可以大大提高分类的性能。在本文中，我们提出了基于HTC的层次微调神经元LSTM（HFT-ONLSTM）的文本和父类别的联合嵌入。我们的方法充分利用了高级和下层标签之间的连接。实验表明，我们的模型以较低的计算成本优于最先进的分层模型。
Text classification has become increasingly challenging due to the continuous refinement of classification label granularity and the expansion of classification label scale. To address that, some research has been applied onto strategies that exploit the hierarchical structure in problems with a large number of categories. At present, hierarchical text classification (HTC) has received extensive attention and has broad application prospects. Making full use of the relationship between parent category and child category in text classification task can greatly improve the performance of classification. In this paper, We propose a joint embedding of text and parent category based on hierarchical fine-tuning ordered neurons LSTM (HFT-ONLSTM) for HTC. Our method makes full use of the connection between the upper-level and lower-level labels. Experiments show that our model outperforms the state-of-the-art hierarchical model at a lower computation cost.
