Abstract
Social annotation systems enable users to annotate large-scale texts with tags which provide a convenient way to discover, share and organize rich information. However, manually annotating massive texts is in general costly in manpower. Therefore, automatic annotation by tag prediction is of great help to improve the efficiency of semantic identification of social contents. In this paper, we propose a tag prediction model based on convolutional neural networks (CNN) and bi-directional long short term memory (BiLSTM) network, through which, tags of texts can be predicted efficiently and accurately. By Experiments on real-world datasets from a social Q&A community, the results show that the proposed CNN-BiLSTM model achieves state-of-the-art accuracy for tag prediction.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (NSFC) grant funded by the China government, Ministry of Science and Technology(No.61672108).
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Li, B., Wang, Q., Wang, X., Li, W. (2018). Tag Prediction in Social Annotation Systems Based on CNN and BiLSTM. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_32
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DOI: https://doi.org/10.1007/978-3-319-93818-9_32
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