Abstract
In the practice of information retrieval, there are some problems such as the lack of accurate expression of user query requests, the mismatch between document and query and query optimization. Focusing on these problems, we propose the query expansion method based on conceptual semantic space with deep learning, this hybrid query expansion technique include deep learning and pseudocorrelation feedback, use the deep learning and semantic network WordNet to construct query concept tree in the level of concept semantic space, the pseudo-correlation feedback documents are processed by observation window, compute the co-occurrence weight of the words by using the average mutual information and get the final extended words set. The results of experiment show that the expansion algorithm based on conceptual semantic space with deep learning has better performance than the traditional pseudo-correlation feedback algorithm on query expansion.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Wen, K.M., Lu, Z.D., Sun, X.L., and Li, R.X., Research of semantic search: Overview, Comput. Sci., 2008, vol. 5, pp. 1–4.
Maron, M.E. and Kuhns, J.L., On relevance, probabilistic indexing and information retrieval, J. ACM, 1960, vol. 3, pp. 216–244.
Rocchio, J.J., Document Retrieval Systems-Optimization and Evaluation, Harvard, 1966.
Ide, E., New experiments in relevance feedback, in The Smart Retrieval System, Saltion, G., Ed., Englewood Cliffs, NJ: N.J. Prentice Hall Inc., 1971, pp. 337–354.
Robertson, S.E. and Jones, K.S., Relevance weighting of search terms, J. Am. Soc. Inf. Sci., 1976, vol. 3, pp. 129–146.
Attar, R. and Fraenkel, A.S., Local feedback in full-text retrieval systems, J. Assoc. Comput. Mach., 1977, vol. 3, pp. 397–417.
Wu, H. and Salton, G., The estimation of term relevance weights using relevance feedback, J. Doc., 1981, vol. 4, pp. 194–214.
Croft, W.B., What do people want from information retrieval, D-Lib Mag., 1995.
Qiu, Y. and Frei, H.P., Concept Based Query Expansion, 1993, pp. 160–169.
Crouch, C.J. and Yang, B., Experiments in automatic statistical thesaurus construction, Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1992, pp. 77–88.
Kelly, D. and Teevan, J., Implicit feedback for inferring user preference: A bibliography, ACM SIGIR Forum, 2003, pp. 18–28.
Jung, S., Click data as implicit relevance feedback in web search, Inf. Process. Manage., 2007, vol. 3, pp. 791–807.
Jing, Y. and Croft, W.B., An Association Thesaurus for Information Retrieval, University of Massachusetts Amherst, MA, 1994.
Wang, X.Y. and Wei, X.X., Query expansion method based on ontology and local co-occurrence, Comput. Sci., 2017, vol. 1, pp. 214–218.
Li, W.J. and Wang, F., Method of query expansion based on LCA prune semantic tree, Comput. Sci., 2015, vol. 1, pp. 479–483.
Chen, Y.J., Yan, J.F., and Liu, X.S., Social strength learning between users based on spatiotemporal data, Comput. Sci., 2016, vol. 1, pp. 251–254.
Yan, H.Y., Liu, Q.P., and Xiao, L.J., Survey on relevance feedback for information retrieval, Appl. Res. Comput., 2009, vol. 1, pp. 11–14.
Rosenfeld, R., A Maximum Entropy Approach to Adaptive Statistical Language Modelling, Pittsburgh: Carnegie Mellon University, 1994.
Author information
Authors and Affiliations
Corresponding author
Additional information
The article is published in the original.
About this article
Cite this article
Li, W., Wang, S. & Yu, Z. Deep Learning and Semantic Concept Spaceare Used in Query Expansion. Aut. Control Comp. Sci. 52, 175–183 (2018). https://doi.org/10.3103/S0146411618030082
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.3103/S0146411618030082