当前位置: X-MOL 学术Multimed. Tools Appl. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Learning neural networks for text classification by exploiting label relations
Multimedia Tools and Applications ( IF 3.0 ) Pub Date : 2020-05-26 , DOI: 10.1007/s11042-020-09063-6
Jincheng Xu , Qingfeng Du

Text classification has been a fundamental problem in the realm of Natural Language Processing (NLP), and a variety of approaches have been proposed with the development of deep learning. Despite recent progress, most existing approaches deal with the problem of multi-class text classification in a flat way, assuming that text labels are semantically independent. This assumption doesn’t always hold in realistic settings, since there usually exist hierarchical or within-layer dependencies in the latent label space, especially when we consider larger label sets. In this paper, we propose a label clustering algorithm to exploit the underlying structure of label relations, and express the stacked concept relationships in the form of a two-layer label space. Next, we present two different neural network structures to capture inter-layer and intra-layer label relations. The first model HSNN organizes a group of local classifiers in a hierarchical way according to the exploited label space, while the second model LSNN takes advantages of text representations in different granularity levels and the bidirectional inferences with recurrent connections to make predictions. Finally, we evaluate our methods on three benchmark datasets. The results empirically demonstrate that both models are capable of leveraging the exploited label relations to improve text classification performance.



中文翻译:

通过利用标签关系学习神经网络以进行文本分类

文本分类已成为自然语言处理(NLP)领域中的一个基本问题,随着深度学习的发展,提出了多种方法。尽管有最新进展,但假设文本标签在语义上是独立的,大多数现有方法仍以扁平方式处理多类文本分类问题。由于在潜在标签空间中通常存在分层或层内依赖关系,所以这种假设并不总是适用于实际设置,尤其是当我们考虑使用较大的标签集时。在本文中,我们提出了一种标签聚类算法,以利用标签关系的底层结构,并以两层标签空间的形式表示堆叠的概念关系。下一个,我们提出了两种不同的神经网络结构来捕获层间和层内标签关系。第一个模型HSNN根据所利用的标签空间以分层的方式组织一组局部分类器,而第二个模型LSNN利用不同粒度级别的文本表示和具有递归连接的双向推断的优势进行预测。最后,我们在三个基准数据集上评估我们的方法。结果从经验上证明了这两种模型都能够利用被利用的标签关系来改善文本分类性能。而第二个模型LSNN则利用了不同粒度级别的文本表示和具有递归连接的双向推理的优势来进行预测。最后,我们在三个基准数据集上评估我们的方法。结果从经验上证明了这两种模型都能够利用被利用的标签关系来改善文本分类性能。而第二个模型LSNN则利用了不同粒度级别的文本表示和具有递归连接的双向推理的优势来进行预测。最后,我们在三个基准数据集上评估我们的方法。结果从经验上证明了这两种模型都能够利用被利用的标签关系来改善文本分类性能。

更新日期:2020-05-26
down
wechat
bug