Abstract
Hashing, as an efficient approach, has been widely used for large-scale similarity search. Unfortunately, many existing hashing methods based on observed keyword features are not effective for short texts due to the sparseness and shortness. Recently, some researchers try to construct semantic relationship using certain granularity topics. However, the topics of certain granularity are insufficient to preserve the optimal semantic similarity for different types of datasets. On the other hand, tag information should be fully exploited to enhance the similarity of related texts. We, therefore, propose a novel unified hashing approach that the optimal topic features can be selected automatically to be integrated with original features for preserving similarity, and tags are fully utilized to improve hash code learning. We carried out extensive experiments on one short text dataset and even one normal text dataset. The results demonstrate that our approach is effective and significantly outperforms baseline methods on several evaluation metrics.
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Xu, J., Xu, B., Zhao, J., Tian, G., Zhang, H., Hao, H. (2014). Short Text Hashing Improved by Integrating Topic Features and Tags. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_36
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DOI: https://doi.org/10.1007/978-3-319-12640-1_36
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