Choosing document structure weights

Information Processing and Management

Volumn 41, Issue 2, 2005, Pages 243-264

  Trotman, Andrew ^a  

^a UNIVERSITY OF OTAGO   (New Zealand)

Author keywords

Genetic algorithms; Probability model; Structured information retrieval; Vector space model

Indexed keywords

COMPUTER SIMULATION; GENETIC ALGORITHMS; MATHEMATICAL MODELS; PROBABILISTIC LOGICS; PROBABILITY; QUERY LANGUAGES; VECTORS; XML;

PROBABILITY MODELS; QUERIES; STRUCTURAL INFORMATION RETRIEVAL; VECTOR SPACE MODELS;

INFORMATION RETRIEVAL;

EID: 7544224854     PISSN: 03064573     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ipm.2003.10.003     Document Type: Article

References (48)

1. Baeza-Yates, R., Navarro, G., & Vegas, J. (1998). A model and a visual query language for structured text. In Proceedings of the string processing and information retrieval: A South American symposium (pp. 7-13).
2. Bartell, B. T., Cottrell, G. W., & Belew, R. K. (1994). Automatic combination of multiple ranked retrieval systems. In Proceedings of the 17th ACM SIGIR conference on information retrieval (pp. 173-181).
3. Boughanem M., Chrisment C., Tamine L. On using genetic algorithms for multimodal relevance optimization in information retrieval. Journal of the American Society for Information Science and Technology. 53(11):2002;934-942.
4. Boyan, J., Freitag, D., & Joachims, T. (1996). A machine learning architecture for optimizing web search engines. In Proceedings of the AAAI workshop on Internet-based information systems.
5. Bray, T., Paoli, J., & Sperberg-McQueen, C. (1988). Extensible markup language (XML) 1.0, W3C recommendation. Available: http://www.w3.org/TR/REC-xml.
6. Callan, J. P. (1994). Passage-level evidence in document retrieval. In Proceedings of the 17th ACM SIGIR conference on information retrieval (pp. 302-310).
7. Chen H. Machine learning for information retrieval: neural networks, symbolic learning, and genetic algorithms. Journal of the American Society for Information Science. 46(3):1995;194-216.
8. Chinenyanga, T. T., & Kushmerick, N. (2001). Expressive retrieval from XML documents. In Proceedings of the 24th ACM SIGIR conference on information retrieval (pp. 163-171).
9. De Jong, K. A. (1975). An analysis of the behavior of a class of genetic adaptive systems. Unpublished Ph.D., University of Michigan.
10. Fuhr, N., Gövert, N., Kazai, G., & Lalmas, M. (2002). Inex: initiative for the evaluation of XML retrieval. In Proceedings of the ACM SIGIR 2000 workshop on XML and information retrieval.
11. Fuhr, N., & Großjohann, K. (2000). XIRQL an extension of XQL for information retrieval. In Proceedings of the ACM SIGIR 2000 workshop on XML and information retrieval.
12. Fuhr, N., & Großjohann, K. (2001). XIRQL: a query language for information retrieval in XML documents. In Proceedings of the 24th ACM SIGIR conference on information retrieval (pp. 172-180).
13. Fuller, M., Mackie, E., Sacks-Davis, R., & Wilkinson, R. (1993). Structured answers for a large structured document collection. In Proceedings of the 16th ACM SIGIR conference on information retrieval (pp. 204-213).
14. Goldberg D.E. Genetic algorithms in search, optimization and machine learning. 1989;Addison-Wesley.
15. Gordon M. Probabilistic and genetic algorithms in document retrieval. Communications of the ACM. 31(10):1988;1208-1218.
16. Grefenstette J.J. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics. 16(1):1986;122-128.
17. Harman, D. (1993). Overview of the first TREC conference. In Proceedings of the 16th ACM SIGIR conference on information retrieval (pp. 36-47).
18. Holland J.H. Adaptation in natural and artificial systems. 1975;University of Michigan Press, Ann Arbor.
19. Horng J.-T., Yeh C.-C. Applying genetic algorithms to query optimization in document retrieval. Information Processing & Management. 36(5):2000;737-759.
20. Kaszkiel, M., & Zobel, J. (1997). Passage retrieval revisited. In Proceedings of the 20th ACM SIGIR conference on information retrieval (pp. 178-185).
21. Kaszkiel M., Zobel J. Effective ranking with arbitrary passages. Journal of the American Society for Information Science and Technology. 52(4):2001;344-364.
22. Khuri, S., Bäck, T., & Heitkötter, J. (1994). An evolutionary approach to combinatorial optimization problems. In Proceedings of the 22nd annual ACM computer science conference (pp. 66-73).
23. Kim, Y.-H., Kim, S., Eom, J.-H., & Zhang, B.-T. (2000). SCAI experiments on TREC-9. In Proceedings of the 9th text retrieval conference (TREC-9) (pp. 392-399).
24. Kim, S., & Zhang, B.-T. (2000). Web-document retrieval by genetic learning of importance factors for HTML tags. In Proceedings of the PRICAI-2000 workshop on text and web mining (pp. 13-23).
25. Kim, S., & Zhang, B.-T. (2001). Evolutionary learning of web-document structure for information retrieval. In Proceedings of the 2001 congress on evolutionary computation (CEC2001) (pp. 1253-1260).
26. Kotsakis, E. (2002). Structured information retrieval in XML documents. In Proceedings of the ACM symposium on applied computing (pp. 663-667).
27. Meuss, H., & Strohmaier, C. (1999). Improving index structures for structured document retrieval. In Proceedings of the 21st annual colloquium on IR research (IRSG'99).
28. Moody J. Prediction risk and architecture selection for neural networks. Cherkassky V., Friedman J.H., Wechsler H. Statistics to neural networks: Theory and pattern recognition applications. 1994;Springer-Verlag.
29. Morgan, J., & Kilgour, A. (1996). Personalising on-line information retrieval support with a genetic algorithm. In A. Moscardini, & P. Smith (Eds.), PolyModel 16: Applications of artificial intelligence (pp. 142-149).
30. Pathak, P., Gordon, M. D., & Fan, W. (2000). Effective information retrieval using genetic algorithms based matching function adaptation. In Proceedings of the 33rd Hawaii international conference on system science.
31. Rapela, J. (2001). Automatically combining ranking heuristics for HTML documents. In Proceedings of the 3rd international workshop on Web information and data management (pp. 61-67).
32. Robertson S.E., Sparck-Jones K. Relevance weighting of search terms. Journal of the American Society for Information Science. 27(3):1976;129-146.
33. Robertson, S. E., Walker, S., Beaulieu, M. M., Gatford, M., & Payne, A. (1995). Okapi at TREC-4. In Proceedings of the 4th text retrieval conference (TREC-4) (pp. 73-96).
34. Salton G., Wong A., Yang C.S. A vector space model for automatic indexing. Communications of the ACM. 18(11):1975;613-620.
35. Savoy, J., & Vrajitoru, D. (1996). Evaluation of learning schemes used in information retrieval (CR-I-95-02). Université de Neuchâtel, Faculté de droit et des Sciences Économiques.
36. Schlieder, T., & Meuss, H. (2000). Result ranking for structured queries against XML documents. In Proceedings of the DELOS workshop on information seeking, searching and querying in digital libraries.
37. Schlieder T., Meuss H. Querying and ranking XML documents. Journal of the American Society for Information Science and Technology. 53(6):2002;489-503.
38. Shin, D., Jang, H., & Jin, H. (1998). Bus: an effective indexing and retrieval scheme in structured documents. In Proceedings of the 3rd ACM international conference on digital libraries (pp. 235-243).
39. Thom, J. A., Zobel, J., & Grima, B. (1995). Design of indexes for structured documents (CITRI/TR-95-8). Melbourne, Australia: Department of Computer Science, RMIT.
40. Trotman, A. (in press). Searching structured documents. Information Processing & Management. DOI: 10.1016/S0306-4573(03)00041-4, available on ScienceDirect since 6 June 2003.
41. Vrajitoru D. Crossover improvement for the genetic algorithm in information retrieval. Information Processing & Management. 34(4):1998;405-415.
42. Vrajitoru D. Large population or many generations for genetic algorithms? Implications in information retrieval. Crestani F., Pasi G. Soft computing in information retrieval. Techniques and applications. 2000;199-222 Physica-Verlag.
43. Weiss S.M., Kulikowski C.A. Computer systems that learn: Classification and prediction methods from statistics, neural nets, machine learning, and expert systems. 1991;Morgan Kaufman.
44. Wilkinson, R. (1994). Effective retrieval of structured documents. In Proceedings of the 17th ACM SIGIR conference on information retrieval (pp. 311-317).
45. Wilkinson, R., & Zobel, J. (1994). Comparison of fragmentation schemes for document retrieval. In Proceedings of the 3th text retrieval conference (TREC-3) (pp. 81-84).
46. Wolff, J. E., Flörke, H., & Cremers, A. B. (1999). Xpres: A ranking approach to retrieval on structured documents (IAI-TR-99-12). Bonn: University of Bonn.
47. Wolff, J. E., Flörke, H., & Cremers, A. B. (2000). Searching and browsing collections of structural information. In Proceedings of the IEEE advances in digital libraries (pp. 141-150).
48. Yang, J., Korfhage, R., & Rasmussen, E. (1992). Query improvement in information retrieval using genetic algorithms - a report on the experiments of the TREC project. In Proceedings of the 1st text retrieval conference (TREC-1) (pp. 31-58).