Abstract
This paper is concerned with the multi-label text classification (MLTC) task, whose goal is to assign one or more categorical labels to a document. The two critical characteristics of this task are the intra-class and inter-class information. The former means the distribution of samples belonging to the same category, and the latter models the relationships between labels such as label co-occurrence and label hierarchy. However, previous methods focus on either of them instead of combining both. This paper proposes a novel two-branch architecture to capture both intra-class and inter-class information. Experimental results show that considering both information improves the performance of the model. Besides, our model achieves competitive results on two widely used datasets.
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Wu, X., Ru, D., Zhang, W., Yu, Y., Feng, Z. (2023). Aggregating Intra-class and Inter-class Information for Multi-label Text Classification. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1791. Springer, Singapore. https://doi.org/10.1007/978-981-99-1639-9_20
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