Automatic thesaurus construction using Bayesian networks

Information Processing and Management

Volumn 32, Issue 5, 1996, Pages 543-553

  Park, Young C ^a   Choi, Key Sun ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA STRUCTURES; MATHEMATICAL MODELS; STATISTICAL METHODS; VECTORS; VOCABULARY CONTROL;

AUTOMATIC THESAURUS CONSTRUCTION; BAYESIAN NETWORK; INFERENCE NETWORK;

COMPUTER NETWORKS;

EID: 0030244429     PISSN: 03064573     EISSN: None     Source Type: Journal    
DOI: 10.1016/0306-4573(96)00026-X     Document Type: Article

References (20)

1. Cooper, G. (1990). The computational complexity of probabilistic inferencing using Bayesian networks. Artificial Intelligence, 42, 393-405.
2. Crouch, C. J. (1990). An approach to the automatic construction of global thesauri. Information Processing & Management, 26(5), 629-640.
3. Fox, E. (1986). Building the CODER lexicon: The Collins English Dictionary and its adverb definitions. Tech. Report 86-23, Department of Computer Science, VPI&SU. Virginia Tech.
4. Gelfand, A. E., & Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85(410), 298-409.
5. Han, Y. S., & Choi, K.-S. (1993). Lexical concept acquisition from collocation map. In SIGLEX Workshop on Acquisition of Lexical Knowledge from Text, 31st Annual Meeting of the ACL, (pp. 22-31). Ohio State University, Columbus, Ohio.
6. Han, Y. S., Park, Y. C., & Choi, K.-S. (1996). Efficient inferencing for sigmoid Bayesian networks. Applied Intelligence, 6(4).
7. Heckerman, D. (1991). Probabilistic Similarity networks. Cambridge, MA: MIT Press.
8. Hindle, D. (1990). Noun classification from predicate-argument structures. In 28th Annual meeting of the Association for Computational Linguistics (pp. 268-275). Morristown, NJ: Association for Computational Linguistics.
9. McGill, M. et al. (1979). An Evaluation of Factors Affecting Document Ranking by Information Retrieval Systems. Project report, Syracuse University School of Information Studies, Syracuse, NY.
10. Metropolis, N., Rosenbluth, A. W., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21(6), 1087-1092.
11. Neal, R. M. (1992). Connectionist learning of belief networks. Artificial Intelligence, 56, 71-113.
12. Pereira, F., Tishby, N., & Lee, L. (1993). Distributional clustering of english words. In 31st Annual Meeting of the Association for Computational Linguistics (pp. 183-190). Ohio State University, Columbus, Ohio.
13. Salton, G. (1989). Automatic text processing: The transformation, analysis, and retrieval of information by computer. New York: Addison-Wesley.
14. Salton, G., Yang, C., & Yu, C. T. (1975). A theory of term importance in automatic text analysis. Journal of the American Society for Information Science, 26(1), 33-44.
15. Shachter, R. D. (1988). Probabilistic inferencing and influence diagrams. Operations Research, 36(4), 589-604.
16. Soergel, D. (1974). Automatic and semi-automatic methods as an aid in the construction of indexing languages and thesauri. International Classifications, 1(1), 34-39.
17. Sparc Jones, K., & Bates, R. G. (1971). What makes an automatic keyword classification effective? Journal of the American Society for Information Science, 22(3), 166-175.
18. Sparc Jones, K., & Tait, T. I. (1984). Automatic search term variant generation. Journal of Documentation 40(1) 50-66.
19. Spiegelhalter, D. J., & Lauritzen, S. L. (1990). Sequential updating of conditional probabilities on directed graphical structures. Networks, 20, 579-605.
20. York, J. (1992). Use of the Gibbs sampler in expert systems. Artificial Intelligence, 56, 115-130.