Comparing Boolean and probabilistic information retrieval systems across queries and disciplines

Journal of the American Society for Information Science

Volumn 48, Issue 2, 1997, Pages 143-156

  Losee, Robert M ^a  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BOOLEAN FUNCTIONS; COMPUTER SIMULATION; ONLINE SEARCHING; PROBABILISTIC LOGICS; QUERY LANGUAGES; VARIATIONAL TECHNIQUES; VECTORS;

RANKING DOCUMENTS; TERM WEIGHTING SYSTEMS;

INFORMATION RETRIEVAL SYSTEMS;

EID: 0031077471     PISSN: 00028231     EISSN: None     Source Type: Journal    
DOI: 10.1002/(SICI)1097-4571(199702)48:2<143::AID-ASI5>3.0.CO;2-Y     Document Type: Review

References (49)

1. Bartell, B. T., Cottrell, G. W., & Belew, R. K. (1994). Automatic combination of multiple ranked retrieval systems. In ACM Annual Conference on Research and Development in Information Retrieval (pp. 173-181). New York: ACM Press.
2. Becher, T. (1987). Disciplinary discourse. Studies in Higher Education, 12(3), 261-274.
3. Belkin, N. J., Cool, C., Croft, W. B., & Callan, J. P. (1993). The effect of multiple query representations on information retrieval performance. In ACM Annual Conference on Research and Development in Information Retrieval (pp. 339-346). New York: ACM Press.
4. Bonzi, S. (1984). Terminological consistency in abstract and concrete disciplines. Journal of Documentation, 40(4), 247-263.
5. Bookstein, A. (1982). Explanation and generalization of vector models in information retrieval. In Research and Development in Information Retrieval: Proceedings of the 5th International Conference on Information Retrieval. Springer-Verlag: Berlin.
6. Bookstein, A. (1983). Information retrieval: A sequential learning process. Journal of the American Society for Information Science, 34, 331-342.
7. Bookstein, A. (1985). Implications of Boolean structure for probabilistic retrieval. In Proceedings of the Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 11-17). New York: ACM Press.
8. Bovey, J. D., & Robertson, S. E. (1984). An algorithm for weighted searching on a Boolean system. Information Technology, 3(2), 84-87.
9. Chow, C., & Liu, C. (1968). Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, IT-14(3), 462-467.
10. Chow, D., & Yu, C. (1982). On the construction of feedback queries. Journal of the Association for Computing Machinery, 29, 127-151.
11. Cooper, W. S. (1995). Some inconsistencies and misidentified modeling assumptions in probabilistic information retrieval. ACM Transactions on Information Systems, 13(1), 100-111.
12. Croft, W. B. (1986). Boolean queries and term dependencies in probabilistic retrieval models. Journal of the American Society for Information Science, 37, 71-77.
13. Evans, R. (1994). Beyond Boolean: Relevance ranking, natural language and the new search paradigm. In Proceedings of the Fifteenth National Online Meeting (pp. 121-128). Medford, NJ: Learned Information.
14. Everett, D. M., & Carter, S. C. (1992). Topology of document retrieval systems. Journal of the American Society for Information Science, 43, 658-673.
15. Fox, E. A., & Winett, S. G. (1990). Using vector and extended Boolean matching in an expert system for selecting foster homes. Journal of the American Society for Information Science, 41, 10-26.
16. Gupta, P. D. (1987). Boolean interpretation of conjunctions for document retrieval. Journal of the American Society for Information Science, 38, 245-254.
17. Haas, S. W. (1995). Domain terminology patterns in different disciplines: Evidence from abstracts. In Proceedings of the Fourth Annual Symposium on Document Analysis and Information Retrieval (pp. 137-146). Information Science Research Institute: Las Vegas, NV.
18. Kneale, W., & Kneale, M. (1986). The development of logic. Oxford: Clarendon Press.
19. Lam, K., & Yu, C. T. (1982). A clustered search algorithm incorporating arbitrary term dependencies. ACM Transactions on Database Systems, 7, 500-508.
20. Lee, J. H. (1994). Properties of extended Boolean models in information retrieval. In ACM Annual Conference on Research and Development in Information Retrieval (pp. 182-190). New York: ACM Press.
21. Lee, J. H. (1995). Combining multiple evidence from different properties of weighting schemes. In ACM Annual Conference on Research and Development in Information Retrieval (pp. 180-188). New York: ACM Press.
22. Losee, R. M. (1988). Parameter estimation for probabilistic document retrieval models. Journal of the American Society for Information Science, 39, 8-16.
23. Losee, R. M. (1991). An analytic measure predicting information retrieval system performance. Information Processing and Management, 27(1), 1-13.
24. Losee, R. M. (1994a). Term dependence: Truncating the Bahadur Lazarsfeld expansion. Information Processing and Management, 30(2), 293-303.
25. Losee, R. M. (1994b). Upper bounds for retrieval performance and their use measuring performance and generating optimal Boolean queries: Can it get any better than this? Information Processing and Management, 30(2), 193-203.
26. Losee, R. M. (1995a). Determining information retrieval performance without experimentation. Information Processing and Management, 31(4), 555-572.
27. Losee, R. M. (1995b). The development and migration of concepts from donor to borrower disciplines: Sublanguage term use in hard & soft sciences. In M. E. D. Koenig & A. Bookstein (Eds.), Proceedings of the Fifth International Conference on Scientometrics and Informetrics (pp. 265-274). Medford, NJ: Learned Information.
28. Losee, R. M. (1996). Evaluating retrieval performance given database and query characteristics: Analytic determination of performance surfaces. Journal of the American Society for Information Science, 47, 95-105.
29. Losee, R. M., & Bookstein, A. (1988). Integrating Boolean queries in conjunctive normal form with probabilistic retrieval models. Information Processing and Management, 24(3), 315-321.
30. Losee, R. M., Bookstein, A., & Yu, C. T. (1986). Probabilistic models for document retrieval: A comparison of performance on experimental and synthetic databases. In ACM Annual Conference on Research and Development in Information Retrieval (pp. 258-264) New York: ACM Press.
31. Losee, R. M., & Haas, S. W. (1995). Sublanguage terms: Dictionaries, usage, and automatic classification. Journal of the American Society for Information Science, 46, 519-529.
32. Markey, K., & Cochrane. P. A. (1981). ONTAP: An online training and practice manual for ERIC database searchers (2nd ed.). ERIC Clearinghouse on Information Resources, Syracuse University, Syracuse, NY.
33. Moon, S. B. (1993). Enhancing retrieval performance of full-text retrieval systems using relevance feedback. Unpublished doctoral dissertation, University of North Carolina, Chapel Hill.
34. Nie, J. (1989). An information retrieval model based on modal logic. Information Processing and Management, 25 (5),477-491.
35. Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14(3), 130-137.
36. Radecki, T. (1979). Fuzzy set theoretical approach to document retrieval. Information Processing and Management, 15, 247-259.
37. Radecki, T. (1982). A probabilistic approach to information retrieval in systems with Boolean search request formulations. Journal of the American Society for Information Science, 33, 365-370.
38. Robertson, S. E., & Thompson, C. L. (1990). Weighted searching: the CIRT experiment. In Informatics 10: Prospects for intelligent retrieval (pp. 153-166). London: ASLIB.
39. Robertson, S. E., Thompson, C. L., Macaskill, M. J., & Bovey, J. D. (1986). Weighting, ranking and relevance feedback in a front-end system. Journal of Information Science, 12(1/2), 71-75.
40. Robertson, S. E. (1977). The probability ranking principle in IR. Journal of Documentation, 33(4), 294-304.
41. Robertson, S. E., & Sparck Jones, K. (1976). Relevance weighting of search terms. Journal of the American Society for Information Science, 27, 129-146.
42. Sager, N. (1981). Information structures in texts of a sublanguage. In L. Lunin, M. Henderson, & H. Wooster (Eds.), Proceedings of the 44th ASIS Annual Meeting (pp. 199-201). White Plains, NY: Knowledge Industry Publications.
43. Salton, G. (1984). The use of extended Boolean logic in information retrieval (Tech. Rep. TR 84-588). Ithaca, NY: Cornell University.
44. Smeaton, A. F. (1984). Relevance feedback and a fuzzy set of search terms in an information retrieval system. Information Technology: Research and Development, 3(1), 15-23.
45. Spink, A. (1995). Term relevance feedback and mediated database searching: Implications for information retrieval practice and systems design. Information Processing and Management, 31(2), 161-171.
46. Tibbo, H. R. (1992). Abstracting across the disciplines: A content analysis of abstracts from the natural sciences, the social sciences, and the humanities with implications for standardization and online information retrieval. Library and Information Science Research, 14, 31-56.
47. Turtle, H. (1994). Natural language vs. Boolean query evaluation: A comparison of retrieval performance. In ACM Annual Conference on Research and Development in Information Retrieval (pp. 212-220). New York: ACM Press.
48. Van Rijsbergen, C. (1977). A theoretical basis for use of co-occurrence data in information retrieval. Journal of Documentation, 33(2), 106-119.
49. Yu, C. T., Buckley, C., Lam, K., & Salton, G. (1983). A generalized term dependence model in information retrieval. Information Technology: Research and Development, 2(4), 129-154.