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Multi-label text classification (MLTC) aims to tag most relevant labels for the given document. In this paper, we propose a Label-Specific Attention Network (LSAN) to learn the new document representation. LSAN takes advantage of label semantic information to determine the semantic connection between labels and document for constructing labelspecific document representation. Meanwhile, the self-attention mechanism is adopted to identify the label-specific document representation from document content information. In order to seamlessly integrate the above two parts, an adaptive fusion strategy is designed, which can effectively output the comprehensive document representation to build multilabel text classifier. Extensive experimental results on four benchmark datasets demonstrate that LSAN consistently outperforms the stateof-the-art methods, especially on the prediction of low-frequency labels. The code and hyper-parameter settings are released to facilitate other researchers 1 .

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One of the main research problems in heterogeneous transfer learning is to determine whether a given source domain is effective in transferring knowledge to a target domain, and then to determine how much of the knowledge should be transferred from a source domain to a target domain. The main objective of this paper is to solve this problem by evaluating the relatedness among given domains through transferred weights. We propose a novel method to learn such transferred weights with the aid of co-occurrence data, which contain the same set of instances but in different feature spaces. Because instances with the same category should have similar features, our method is to compute their principal components in each feature space such that co-occurrence data can be rerepresented by these principal components. The principal component coefficients from different feature spaces for the same instance in the co-occurrence data have the same order of significance for describing the category information. By using these principal component coefficients, the Markov Chain Monte Carlo method is employed to construct a directed cyclic network where each node is a domain and each edge weight is the conditional dependence from one domain to another domain. Here, the edge weight of the network can be employed as the transferred weight from a source domain to a target domain. The weight values can be taken as a prior for setting parameters in the existing heterogeneous transfer learning methods to control the amount of knowledge transferred from a source domain to a target domain. The experimental results on synthetic and real-world data sets are reported to illustrate the effectiveness of the proposed method that can capture strong or weak relations among feature spaces, and enhance the learning performance of heterogeneous transfer learning.

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