In this paper, we transform tag recommendation into a word-based text generation problem and introduce a sequence-to-sequence model. The model inherits the advantages of LSTM-based encoder for sequential modeling and attention-based decoder with local positional encodings for learning relations globally. Experimental results on Zhihu datasets illustrate the proposed model outperforms other state-of-the-art text classification based methods.
CITATION STYLE
Shi, X., Huang, H., Zhao, S., Jian, P., & Tang, Y. K. (2019). Tag Recommendation by Word-Level Tag Sequence Modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11448 LNCS, pp. 420–424). Springer Verlag. https://doi.org/10.1007/978-3-030-18590-9_58
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