Abstract
We develop a deep neural network model to encode document semantic into compact binary codes with the elegant property that semantically similar documents have similar embedding codes. The deep learning model is constructed with three stacked auto-encoders. The input of the lowest auto-encoder is the representation of word-count vector of a document, while the learned hidden features of the deepest auto-encoder are thresholded to be binary codes to represent the document semantic. Retrieving similar document is very efficient by simply returning the documents whose codes have small Hamming distances to that of the query document. We illustrate the effectiveness of our model on two public real datasets – 20NewsGroup and Wikipedia, and the experiments demonstrate that the compact binary codes sufficiently embed the semantic of documents and bring improvement in retrieval accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Anderson, J.A., Davis, J.: An introduction to neural networks. MIT Press (1995)
Bengio, Y.: Learning deep architectures for AI. Foundations and Trends in Machine Learning 2(1), 1–127 (2009)
Blei, D.M., Ng, A.Y., Jordan, M.I., Lafferty, J.: Latent dirichlet allocation. Journal of Machine Learning Research 3 (2003)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society for Information Science 41(6), 391–407 (1990)
Hinton, G.E., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)
Hofmann, T.: Probabilistic latent semantic analysis. In: Proc. of Uncertainty in Artificial Intelligence, UAI 1999, pp. 289–296 (1999)
Salakhutdinov, R., Hinton, G.E.: Semantic hashing. Int. J. Approx. Reasoning 50(7), 969–978 (2009)
Salton, G.: Developments in automatic text retrieval. Science 253(5023), 974–980 (1991)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. In: Information Processing and Management, pp. 513–523 (1988)
Wu, W., Li, H., Wang, H., Zhu, K.Q.: Probase: a probabilistic taxonomy for text understanding. In: SIGMOD Conference, pp. 481–492 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Yu, Z., Zhao, X., Wang, L. (2014). Encoding Document Semantic into Binary Codes Space. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_59
Download citation
DOI: https://doi.org/10.1007/978-3-319-08010-9_59
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08009-3
Online ISBN: 978-3-319-08010-9
eBook Packages: Computer ScienceComputer Science (R0)