With the rapid growth of the number of Web services on the Internet, how to classify Web services correctly and efficiently become particularly important in service management tasks, such as service discovery, service selection, service ranking, and service recommendation. Existing functionality-based service classification techniques have some drawbacks: (1) the keyword order and context information are not considered; (2) the embedding features of keywords are taken as equal importance to learn the classification model; (3) the topic number is hard to determine manually. Due to these drawbacks, the accuracy of service classification needs to be improved further. At present, deep learning techniques show the strong power in modeling complex and nonlinear function relationship. Thus, to address the problems above, this paper exploits attention mechanism to combine the local implicit state vector of Bidirectional Long Short-Term Memory Network (BiLSTM) and the global hierarchical Dirichlet process (HDP) topic vector, and proposes a Web service classification approach with topical attention-based BiLSTM. Specifically, BiLSTM is used to automatically learn the keyword feature representations of Web services. Then, the topic vectors of Web service documents are obtained with HDP by offline training, and topic attention mechanism is adopted to strengthen the feature representation by discriminating the importance or weight of different keywords in Web service documents. Finally, the enhanced Web service feature representation is used as the input of a softmax neural network layer to perform the classification prediction for Web services. Extensive experiments are conducted to validate the effectiveness of the proposed approach.

中文翻译：

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Tatt-BiLSTM：基于主题注意力​​的 BiLSTM 的 Web 服务分类

随着互联网上Web服务数量的快速增长，如何正确有效地对Web服务进行分类，在服务发现、服务选择、服务排序、服务推荐等服务管理任务中变得尤为重要。现有的基于功能的服务分类技术有一些缺点：（1）没有考虑关键字顺序和上下文信息；(2) 将关键词的嵌入特征与学习分类模型同等重要；(3) 主题编号难以人工确定。由于这些缺点，服务分类的准确性需要进一步提高。目前，深度学习技术在建模复杂非线性函数关系方面表现出强大的威力。因此，为了解决上述问题，本文利用注意力机制将双向长短期记忆网络（BiLSTM）的局部隐式状态向量和全局分层狄利克雷过程（HDP）主题向量相结合，提出了一种基于主题注意力​​的BiLSTM的Web服务分类方法。具体来说，BiLSTM 用于自动学习 Web 服务的关键字特征表示。然后，利用HDP通过离线训练得到Web服务文档的主题向量，采用主题注意力​​机制，通过区分Web服务文档中不同关键字的重要性或权重来加强特征表示。最后，将增强的 Web 服务特征表示用作 softmax 神经网络层的输入，对 Web 服务进行分类预测。