Advertisement

Artificial Intelligence and Law

, Volume 3, Issue 1–2, pp 5–54 | Cite as

Text retrieval in the legal world

  • Howard Turtle
Article

Abstract

The ability to find relevant materials in large document collections is a fundamental component of legal research. The emergence of large machine-readable collections of legal materials has stimulated research aimed at improving the quality of the tools used to access these collections. Important research has been conducted within the traditional information retrieval, the artificial intelligence, and the legal communities with varying degrees of interaction between these groups. This article provides an introduction to text retrieval and surveys the main research related to the retrieval of legal materials.

Key words

Bayesian inference networks natural language probability 

References

  1. Ashley, K. D. 1990.Modeling Legal Argument: Reasoning with Cases and Hypotheticals. Cambridge, MA: MIT Press.Google Scholar
  2. Belew, R. K. 1986. Adaptive Information Retrieval: Machine Learning in Associative Networks. PhD thesis, Computer Science Department, University of Michigan.Google Scholar
  3. Belew, R. K. 1987. A Connectionist Approach to Conceptual Information Retrieval. InThe First International Conference on Artificial Intelligence and the Law: Proceedings of the Conference, 116–126. ACM Press.Google Scholar
  4. Belew, R. K. 1989. Adaptive Information Retrieval: Using a Connectionist Representation to Retrieve and Learn About Documents. InProceedings of the 12 th Annual International Conference on Research and Development in Information Retrieval, eds. N. J. Belkin & C. J. van Rijsbergen, 11–20, New York, NY. ACM.Google Scholar
  5. Besnard, P. 1989.Introduction to Default Logic. Springer Verlag.Google Scholar
  6. Bing, J. 1987a. Designing Text Retrieval Systems for ‘Conceptual Searching’. InThe First International Conference on Artificial Intelligence and the Law: Proceedings of the Conference, 43–51. ACM Press.Google Scholar
  7. Bing, J. 1987b. The Performance of Legal Text Retrieval Systems: The curse of Boole.Law Library Journal 79(2): 187–202.Google Scholar
  8. Bing, J. & Harvold, T. 1977.Legal Decisions and Information Systems. Universitetsforlaget, Oslo, Norway.Google Scholar
  9. Blair, D. C. 1990.Language and Representations in Information Retrieval. Amsterdam: Elsevier Science Publishers.Google Scholar
  10. Blair, D. C. & Maron, M. E. 1985. An Evaluation of Retrieval Effectiveness for a Full-Text Document Retrieval Systems.Communications of the ACM 28(3): 290–299.Google Scholar
  11. Bookstein, A. 1980. Fuzzy Requests: An Approach to Weighted Boolean Searches.Journal of the American Society for Information Science 30(4): 240–247.Google Scholar
  12. Bookstein, A. & Swanson, D. R. 1974. Probabilistic Models for Automatic Indexing.Journal of the American Society for Information Science 25: 312–318.Google Scholar
  13. Brachman, R. J. & McGuiness, D. L. 1988. Knowledge Representation Connectionism, and Conceptual Retrieval. In Proceedings ofThe Eleventh International Conference on Research and Development in Information Retrieval, 161–174. New York, NY. ACM.Google Scholar
  14. Brauen, T. L. 1971. Document Vector Modification. InThe SMART Retrieval System — Experiments in Automatic Document Processing, ed. G. Salton, 456–484. Prentice Hall.Google Scholar
  15. Bruza, P. D. & van der Gaag, L. 1993. Efficient Context-Sensitive Plausible Inference for Information Disclosure. In Proceedings ofThe Sixteenth Annual ACM SIGIR Conference on Research and Development in Information Retrieval, eds. Korfhage, R., Rasmussen, E. & Willett, P. 12–21.Google Scholar
  16. Buckley, C., Allan, J. & Salton, G. 1994. Automatic Routing and Ad-hoc Retrieval Using SMART: TREC-2. InThe Second Text Retrieval Conference (TREC-2), ed. D. K. Harman, 45–56. National Institute of Standards and Technology. Proceedings available as NIST Special Publication 500–215.Google Scholar
  17. Callan, J. P. & Croft, W. B. 1993. An Approach to Incorporating CBR Concepts in IR systems. InCase-Based Reasoning and Information Retrieval — Exploring the Opportunities for Technology Sharing, 28–34. AAAI.Google Scholar
  18. Callan, J. P., Croft, W. B. & Harding, S. M. 1992. The INQUERY Retrieval System. In Proceedings ofThe Third International Conference on Database and Expert Systems Applications, 78–83. Springer-Verlag.Google Scholar
  19. Chang, J.-S., Tseng, T.-Y., Cheng, Y., Chen, H.-C. & Cheng, S.-D. 1992. A Corpus-based Statistical Approach to Automatic Book Indexing. In Proceedings ofThe Third Conference on Applied Natural Language Processing, 147–151. Association for Computational Linguistics. Trento, Italy.Google Scholar
  20. Charniak, E. 1993.Statistical Language Learning. Cambridge, MA: MIT Press.Google Scholar
  21. Chiaramella, Y. & Nie, J. 1990. A Retrieval Model Based on an Extended Modal Logic and its Application to the RIME Experimental Approach. In Proceedings ofThe 13 th International Conference on Research and Development in Information Retrieval, ed. J.-L. Vidick, 25–43. ACM.Google Scholar
  22. Church, K. W. 1988. A Stochastic Parts Program and Noun Phrase Parser for Unrestricted Text. In Proceedings ofThe Second Conference on Applied Natural Language Processing, 136–143.Google Scholar
  23. Church, K. W. & Gale, W. A. 1991. A Comparison of the Enhanced Good-Turing and Deleted Estimation Methods for Estimating Probabilities of English Bigrams.Computer Speech and Language, 5: 19–54.Google Scholar
  24. Conrad, J. G. & Utt, M. H. 1994. A System for Discovering Relationships by Feature Extraction from Text Databases. In Proceedings of The Seventeenth Annual International Conference on Research and Development in Information Retrieval, eds. W. B. Croft & C. J. van Rijsbergan, 260–270, London. Springer-Verlag.Google Scholar
  25. Cooper, W. S. 1971. A Definition of Relevance for Information Retrieval.Information Storage and Retrieval, 7: 19–37.Google Scholar
  26. Croft, W. B. 1983. Experiments with Representation in a Document Retrieval System.Information Technology: Research and Development, 2: 1–21.Google Scholar
  27. Croft, W. B. 1986. Boolean Queries and Term Dependencies in Probabilistic Retrieval Models.Journal of the American Society for Information Science, 37(2): 71–77.Google Scholar
  28. Croft, W. B., Callan, J. & Broglio, J. 1994. TREC-2 Routing and Ad-Hoc Retrieval Evaluation Using the INQUERY System. InThe Second Text Retrieval Conference (TREC-2), ed. D. K. Harman, 75–84. National Institute of Standards and Technology. Proceedings available as NIST Special Publication 500-215.Google Scholar
  29. Croft, W. B., Smith, L. A. & Turtle, H. R. 1992. A Loosely-Coupled Integration of a Text Retrieval System and an Object-Oriented Database System. In Proceedings ofThe Fifteenth Annual ACM SIGIR Conference on Research and Development in Information Retrieval, eds. N. Belkin, P. Ingwersen & A. M. Pejtersen, 223–232.Google Scholar
  30. Croft, W. B. & Thompson, R. H. 1984. The Use of Adaptive Mechanisms for Selection of search strategies in document retrieval systems. In Proceedings of The ACM/BCS International Conference on Research and Development in Information Retrieval, ed. C. J. van Rijsbergen, 95–110.Google Scholar
  31. Croft, W. B. & Turtle, H. R. 1992. Text Retrieval and Inference. InText-Based Intelligent Systems, chapter 7, ed. P. S. Jacobs, 127–155. Lawrence Erlbaum Associates.Google Scholar
  32. Croft, W. B., Turtle, H. R. & Lewis, D. D. 1991. The Use of Phrases and Structured Queries in Information Retrieval. In Proceedings ofThe Fourteenth International Conference on Research and Development in Information Retrieval, 32–45. ACM.Google Scholar
  33. Dabney, D. P. 1986. The Curse of Thamus: An Analysis of Full-Text Document Retrieval.Law Library Journal, 78: 5–40.Google Scholar
  34. Dabney, D. P. 1993. Statistical Modeling of Relevance Judgments for Probabilistic Retrieval of American Case Law. PhD thesis, Library and Information Studies, University of California at Berkeley.Google Scholar
  35. deBessonet, C. G. 1991.A Many-Valued Approach to Deduction and Reasoning for Artificial Intelligence. Kluwer Academic Publishers.Google Scholar
  36. Deerwester, S., Dumais, S. T., Furnas, G. W. & Landauer, T. K. 1990. Indexing by Latent Semantic Analysis.Journal of the American Society for Information Science 41(6): 391–407.Google Scholar
  37. Dick, J. P. 1987. Conceptual Retrieval and Caselaw. InThe First International Conference on Artificial Intelligence and the Law: Proceedings of the Conference, 106–115. ACM Press.Google Scholar
  38. Dick, J. P. 1991. Representation of legal text for conceptual retrieval. InThe Third International Conference on Artificial Intelligence and the Law: Proceedings of the Conference, 244–253. ACM Press.Google Scholar
  39. Dick, J. P. 1992.A Conceptual, Case-Relation Representation of Text for Intelligent Retrieval. PhD thesis, University of Toronto. Available as Technical Report CSRI-265.Google Scholar
  40. Dillon, M., Ulmschneider, J. & Desper, J. 1983. A Prevalence Formula for Automatic Relevance Feedback in Boolean Systems.Information Processing and Management 19(1): 27–36.Google Scholar
  41. Dubois, D. & Prade, H. 1988.Possibility Theory: An Approach to Computerized Processing of Uncertainty. New York, NY: Plenum Press.Google Scholar
  42. Dumais, S. T. 1994. Latent Semantic Indexing (LSI) and TREC-2. InThe Second Text Retrieval Conference (TREC-2), ed. D. K. Harman, 105–116. National Institute of Standards and Technology. Proceedings available as NIST Special Publication 500-215.Google Scholar
  43. Favero, B. D. & Fung, R. 1994. & Fung, R. 1994. Bayesian Inference with Node Aggregation for Information Retrieval. InThe Second Text Retrieval Conference (TREC-2), ed. D. K. Harman, 151–162. National Institute of Standards and Technology. Proceedings available as NIST Special Publication 500-215.Google Scholar
  44. Frakes, W. B. 1992. Stemming Algorithms.In Information Retrieval: Data Structures & Algorithms, chapter 8, eds. W. B. Frakes & R. Baeza-Yates, 131–160. Prentice Hall.Google Scholar
  45. Frisse, M. E. & Cousins, S. B. 1989. Information Retrieval from Hypertext: Update on the Dynamic Medical Handbook Project. InHypertext '89 Proceedings, 199–212.Google Scholar
  46. Fuhr, N. 1989. Models for Retrieval with Probabilistic Indexing.Information Processing and Management 25(1): 55–72.Google Scholar
  47. Fuhr, N. 1993. A Probabilistic Relational Model for the Integration of IR and Databases. In Proceedings ofThe Sixteenth Annual ACM SIGIR Conference on Research and Development in Information Retrieval, eds. R. Korfhage, E. Rasmussen & P. Willett 309–317.Google Scholar
  48. Fuhr, N. & Buckley, C. 1990. Probabilistic Document Indexing from Relevance Feedback Data. In Proceedings ofThe 13th International Conference on Research and Development in Information Retrieval, ed. J.-L. Vidick, 45–61. ACM.Google Scholar
  49. Fuhr, N., Pfeifer, U., Bremkamp, C. & Pollman, M. 1994. Probabilistic Learning Approaches for Indexing and Retrieval with the TREC-2 collection. InThe Second Text Retrieval Conference (TREC-2), ed. D. K. Harman, 67–74. National Institute of Standards and Technology. Proceedings available as NIST Special Publication 500–512.Google Scholar
  50. Fung, R. M., Crawford, S. L., Applebaum, L. A. & Tong, R. M. 1990. An Architecture for Probabilistic Concept-Based Information Retrieval. In Proceeding ofThe 13th International Conference on Research and Development in Information Retrieval, ed. J.-L. Vidick, 455–467. ACM.Google Scholar
  51. Furnas, G. W., Landauer, T. K., Gomez, L. M. & Dumais, S. T. 1987. The Vocabulary Problem in Human-System Communication.Communications of the ACM 30(11): 964–971.Google Scholar
  52. Gelbart, D. & Smith, J. C. 1990. Toward a Comprehensive Legal Information Retrieval System. InDatabase and Expert System Applications: Proceedings of the International Conference, 121–125. Springer-Verlag.Google Scholar
  53. Gelbart, D. & Smith, J. C. 1991a. Beyond Boolean Search: FLEXICON, a Legal Text-based Intelligent System. InThe Third International Conference on Artificial Intelligence and the Law: Proceedings of the Conference, 225–234. ACM Press.Google Scholar
  54. Gelbart, D. & Smith, J. C. 1991b. Current Issues in Text Retrieval: FLEXICON, a Legal Text-Based Intelligent System. In Procedings ofThe AAAI-91 Natural Language Text Retrieval Workshop, 1–5.Google Scholar
  55. Gelbart, D. & Smith, J. C. 1992. Towards Combining Automated Text Retrieval and Case-Based Expert Legal Advice.Law Technology Journal 1(2): 19–24.Google Scholar
  56. Gelbart, D. & Smith, J. C. 1993. FLEXICON: An Evaluation of a Statistical Ranking Model Adapted to Intelligent Legal Text Management. InThe Fourth International Conference on Artificial Intelligent and the Law: Proceedings of the Conference, 142–151. ACM Press.Google Scholar
  57. Gey, F. C. 1994. Inferring Probability of Relevance Using the Method of Logistic Regression. In Proceedings ofThe Seventeenth Annual International Conference on Research and Development in Information Retrieval, eds. W. B. Croft & C. J. van Rijsbergen, 222–231, London: Springer-Verlag.Google Scholar
  58. Hafner, C. D. 1978. An Information Retrieval System Based on a Computer Model of Legal Knowledge. PhD thesis, University of Michigan. Republished by UMI Research Press, Ann Arobor, MI (1981).Google Scholar
  59. Hafner, C. D. 1987. Conceptual Organization of Case Law Knowledge Bases. InThe First International Conference on Artificial Intelligence and the Law: Proceedings of the Conference, 35–42. ACM Press.Google Scholar
  60. Haines, D. & Croft, W. B. 1993. Relevance Feedback and Inference Networks. In Proceedings ofThe Sixteenth International ACM SIGIR Conference on Research and Development in Information Retrieval, eds. R. Korfhage, E. Rasmussen & P. Willet, 2–11.Google Scholar
  61. Harman, D. 1992. Relevance Feedback and Other Query Modification Techniques. InInformation Retrieval: Data Structures & Algorithms, chapter 11, eds. W. B. Frakes & R. Baeza-Yates, 241–263. Prentice Hall.Google Scholar
  62. Harman, D. 1993. Overview of the First Text Retrieval Conference (TREC-1). InThe First Text Retrieval Conference (TREC-1), ed. D. K. Harman, 1–20. National Institute of Standards and Technology. Proceedings available as NIST Special Publication 500-207.Google Scholar
  63. Harper, D. J. 1980. Relevance Feedback in Document Retrieval Systems: An Evaluation of Probabilistic Strategies. PhD thesis, Jesus College, Cambridge, England.Google Scholar
  64. Harrington, W. G. 1985. A Brief History of Computer-Assisted Legal Research.Law Library Journal 77(3): 543–556.Google Scholar
  65. Harter, S. P. 1992.Online Information Retrieval: Concepts, Principles, and Techniques. San Diego, CA: Academic Press.Google Scholar
  66. Hoch, R. 1994. Using IR Techniques for Text Classification in Document Analysis. In Proceedings ofThe Seventeenth Annual International Conference on Research and Development in Information Retrieval, eds. W. B. Croft & C. J. van Rijsbergen, 31–41, London: Springer-Verlag.Google Scholar
  67. Jing, Y. & Croft, W. B. 1994. An Association Thesaurus for Information Retrieval. Technical Report IRL94-x, Information Retrieval Laboratory, Department of Computer Science, University of Massachusetts, Amherst, MA 01003.Google Scholar
  68. Katzer, J., McGill, M. J., Tessier, J. A., Frakes, W. & DasGupta, P. (1982). A Study of the Overlap Among Document Representations.Information Technology: Research and Development 1: 261–274.Google Scholar
  69. Krovetz, R. 1993. Viewing Morphology as an Inference Process. In Proceedings ofThe Sixteenth International ACM SIGIR Conference on Research and Development in Information Retrieval, eds. R. Korfhage, E. Rasmussen & P. Willett, 191–202.Google Scholar
  70. Krovetz, R. & Croft, W. B. 1989. Word Sense Disambiguation Using a Machine Readable Dictionary. InProceedings of The 12 th International Conference on Research and Development in Information Retrieval, eds. N. J. Belkin & C. J. van Rijsbergen, 127–136.Google Scholar
  71. Kwok, K. L. 1989. A Neural Network for Probabilistic Information Retrieval. In Proceedings ofThe 12 th International Conference on Research and Development in Information Retrieval, eds. N. J. Belkin, & C. J. van Rijsbergen, 21–30.Google Scholar
  72. Lauritzen, S. L. & Spiegelhalter, D. J. 1988. Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems.Journal of the Royal Statistical Society B 50(2): 157–224.Google Scholar
  73. Lewis, D. D. 1992.Representation and Learning in Information Retrieval. PhD thesis, Computer Science Department, University of Massachusetts, Amherst, MA 01003.Google Scholar
  74. Lewis, D. D. & Gale, W. A. 1994. A Sequential Algorithm for Training Text Classifiers. In Proceedings ofThe Seventeenth Annual International Conference on Research and Development in Information Retrieval, eds. W. B. Croft & C. J. Rijsbergen, 3–12, London: Springer-Verlag.Google Scholar
  75. Losee, R. M. & Bookstein, A. 1988. Integrating Boolean Queries in Conjunctive Normal Form with Probabilistic Retrieval Models.Journal of the American Society for Information Science 39(3): 315–321.Google Scholar
  76. Lu, X. 1990. Document Retrieval: A Structural Approach.Information Processing and Management 26(2): 209–218.Google Scholar
  77. Luhn, H. P. 1957. A Statistical Approach to Mechanised Encoding and Searching in Library Automation.IBM Journal of Research and Development 1: 309–317.Google Scholar
  78. Margulis, E. L. 1992. N-Poisson Document Modelling. In Proceedings ofThe Fifteenth Annual ACM SIGIR Conference on Research and Development in Information Retrieval, eds. N. Belkin, P. Ingwersen & A. M. Pejtersen, 177–189.Google Scholar
  79. Meadow, C. T. 1992. Text Information Retrieval System. San Diego, CA: Academic Press.Google Scholar
  80. Neapolitan, R. E. 1990. Probabilistic Reasoning in Expert Systems. John Wiley & Sons.Google Scholar
  81. Nie, J.-Y. 1992. Towards a Probabilistic Modal Logic for Semantic-Based Information Retrieval. In Proceedings ofThe Fifteenth Annual ACM SIGIR Conference on Research and Development in Information Retrieval, eds. N. Belkin, P. Ingwersen & A. M. Pejtersen, 140–151.Google Scholar
  82. Nomoto, K., Wakayama, S., Kirimoto, T., Ohashi, Y. & Kondo, M. 1990. A Document Retrieval System Based on Citations Using Fuzzy Graphs.Fuzzy Sets and Systems 38: 207–222.Google Scholar
  83. Ogawa, Y., Morita, T. & Kobayashi, K. 1991. A Fuzzy Document Retrieval System Using the Keyword Connection Matrix and a Learning Method.Fuzzy Sets and Systems 39: 163–179.Google Scholar
  84. Pearl, J. 1988.Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers.Google Scholar
  85. Radecki, T. 1979. Fuzzy Set Theoretical Approach to Document retrieval.Information Processing and Management 15: 247–259.Google Scholar
  86. Rissland, E. L. & Ashley, K. D. 1987. A Case-Based System for Trade Secret Law. In Proceedings ofThe Tenth International Joint Conference on Artificial Intelligence, 60–65. Morgan Kaufmann.Google Scholar
  87. Rissland, E. L., Skalak, D. B. & Friedman, M. T. 1993. Case Retrieval Through Multiple Indexing and Heuristic Search. In Proceedings ofThe Thirteenth International Joint Conference on Artificial Intelligence, xx-xx. Morgan Kaufmann (to appear).Google Scholar
  88. Rissland, E. L., Skalak, D. B. & Friedman, M. T. 1994. Bankxx: Supporting Legal Arguments Through Heuristic Retrieval. Technical Report 94–76, Department of computer Science, University of Massachusetts.Google Scholar
  89. Robertson, S. E. & Sparck Jones, K. 1976. Relevance Weighting of Search Terms.Journal of the American Society for Information Science 27: 129–146.Google Scholar
  90. Robertson, S. E. & Walker, S. 1994. Some Simple Effective Approximations to the 2-Poisson Model for Probabilistic Weighted Retrieval. In Proceedings ofThe Seventeenth Annual International Conference on Research and Development in Information Retrieval, eds. W. B. Croft & C. J. van Rijsbergen, 232–241, London: Springer Verlag.Google Scholar
  91. Rose, D. E. 1991. A Symbolic and Connectionist Approach to Legal Information Retrieval. PhD thesis, University of California, San Diego.Google Scholar
  92. Rose, D. E. & Belew, R. K. 1989. Legal Information Retrieval: A Hybrid Approach. InThe Second International Conference on Artificial Intelligence and the Law: Proceedings of the Conference, 138–146. ACM Press.Google Scholar
  93. Rose, D. E. & Belew, R. K. 1991. A Connectionist and Symbolic Hybrid for Improving Legal Research.International Journal of Man-Machine Studies 35.Google Scholar
  94. Salton, G. 1971.The SMART Retrieval System — Experiments in Automatic Document Processing. Prentice-Hall, Inc.Google Scholar
  95. Salton, G 1988. A Simple Blueprint for Automatic Boolean Query Processing.Information Processing and Management 24(3): 269–280.Google Scholar
  96. Salton, G. 1989.Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley.Google Scholar
  97. Salton, G. & Buckley, C. 1990. Improving Retrieval Performance by Relevance Feedback.Journal of the American Society for Information Science 41(4): 288–297.Google Scholar
  98. Salton, G. & McGill, M. J. 1983.Introduction to Modern Information Retrieval. McGraw-Hill.Google Scholar
  99. Saracevic, T. 1975. Relevance: A Review of and a Framework for Thinking on the Notion in Information Science.Journal of the American Society for Information Science 26(6): 321–343.Google Scholar
  100. Schamber, L., Eisenberg, M. B. & Nilan, M. S. 1990. A Re-Examination of Relevance: Toward a Dynamic, Situational Definition.Information Processing and Management 26(6): 755–776.Google Scholar
  101. Sebastiani, F. 1994. A Probabilistic Terminological Logic for Modelling Information Retrieval. In Proceedings ofThe Seventeenth Annual International Conference on Research and Development in Information Retrieval, eds. W. B. Croft & C. J. van Rijsbergen, 122–130, London: Springer-Verlag.Google Scholar
  102. Somers, H. L. 1987.Valency and Case in Computational Linguistics. Edinburgh: Edinburgh University Press.Google Scholar
  103. Sowa, J. F. 1984.Conceptual Structures: Information Processing in Mind and Machine. Reading, MA. Addison Wesley MA.Google Scholar
  104. Sparck Jones, K. ed. 1981.Information Retrieval Experiment. Butterworth.Google Scholar
  105. Tong, R. M., Reid, C. A., Crowe, G. J. & Douglas, P. R. 1987. Conceptual Legal Document Retrieval Using the RUBRIC System. InThe First International Conference on Artificial Intelligence and the Law: Proceedings of the Conference, 28–34. ACM Press.Google Scholar
  106. Tong, R. M. & Shapiro, D. 1985. Experimental Investigations of Uncertainty in a Rule-Based System for Information Retrieval.International Journal of Man-Machine Studies 22: 265–282.Google Scholar
  107. Toulmin, S. E. 1958.The Uses of Argument. Cambridge, England: Cambridge University Press.Google Scholar
  108. Turtle, H. 1990.Inference Networks for Document Retrieval. PhD thesis, Computer Science Department, University of Massachusetts, Amherst, MA 01003. Available as COINS Technical Report 90-92.Google Scholar
  109. Turtle, H. 1994. Natural Language vs. Boolean Query Evaluation: A Comparison of Retrieval Performance. In Proceedings ofThe Seventeenth Annual International Conference on Research and Development in Information Retrieval, eds. W.B. Croft & C. J. van Rijsbergen, 212–221, London: Springer-Verlag.Google Scholar
  110. Turtle, H. & Croft, W. B. 1990. Inference Networks for Document Retrieval. In Proceedings ofThe 13 th International Conference on Research and Development in Information Retrieval, ed. J.-L. Vidick, 1–24. ACM.Google Scholar
  111. Turtle, H. & Croft, W. B. 1991a. Efficient Probabilistic Inference for Text Retrieval. InRIA091 Conference Proceedings, 644–661.Google Scholar
  112. Turtle, H. & Croft, W. B. 1991b. Evaluation of an Inference Network-Based Retrieval Model.ACM Transactions on Information Systems 9(3): 187–222.Google Scholar
  113. Turtle, H. R. & Croft, W. B. 1992. A Comparison of Text Retrieval Models.Computer Journal 35(3): 279–290.Google Scholar
  114. Tzeras, K. & Hartmann, S. 1993. Automatic Indexing Based on Bayesian Inference Networks. In Proceedings ofThe Sixteenth Annual ACM SIGIR Conference on Research and Development in Information Retrieval, eds. R. Korfhage, E. Rasmussen, & P. Willett, 22–35.Google Scholar
  115. van Rijsbergen, C. J. 1979.Information Retrieval. Butterworths.Google Scholar
  116. van Rijsbergen, C. J. 1986. A Non-Classical Logic for Information Retrieval.Computer Journal 29(6): 481–485.Google Scholar
  117. van Rijsbergen, C. J. 1989. Towards an Information Logic. In Proceedings ofThe Twelfth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, eds. N. J. Belkin & C. J. van Rijsbergen, 77–86, New York: NY. ACM.Google Scholar
  118. Voorhees, E. M. 1994. On Expanding Query Vectors with Lexically Related Words. InThe Second Text Retrieval Conference (TREC-2), ed. D. K. Harman, 223–231. National Institute of Standards and Technology. Proceedings available as NIST Special Publication 500-215.Google Scholar
  119. Voorhees, E. M. & Hou, Y.-W. 1993. Vector Expansion in a Large Collection. InThe First Text Retrieval Conference (TREC-1), ed. D. K. Harman, 343–351. National Institute of Standards and Technology. Proceedings available as NIST Special Publication 500-207.Google Scholar
  120. Waller, W. G. & Kraft, D. H. 1979. A Mathematical Model for Weighted Boolean Retrieval Systems.Information Processing and Management 15(5): 235–245.Google Scholar
  121. Wilson, P. 1973. Situational Relevance.Information Storage and Retrieval 9: 457–471.Google Scholar
  122. Witten, I. H. & Bell, T. C. 1990. Source Models for Natural Language Text.International Journal of Man-Machine Studies 32: 545–579.Google Scholar
  123. Wong, S. K. M., Ziarko, W., Raghavan, V. V. & Wong, P. C. N. 1987. On Modeling of Information Retrieval Concepts in Vector Spaces.ACM Transactions on Database Systems 12(2): 299–321.Google Scholar
  124. Yang, C. S. 1974. On Dynamic Document Space Modification Using Term Discrimination Values. Scientific Report ISR-22, Department of Computer Science, Cornell University.Google Scholar
  125. Zipf, G. K. 1949.Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology. Addison-Wesley, Reading, MA.Google Scholar

Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Howard Turtle
    • 1
  1. 1.West Publishing CompanyEaganUSA

Personalised recommendations