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Outline

Title
Abstract
Figures
Chapter 1 Introduction
Statement and Scope of the Work
Literature Review
Statistical Techniques in Information Retrieval
Conceptual Graph Based Methods
Introduction
Methodology
Experimental Design
Experiment and Results
Results and Discussions
Conclusions
Conclusions and Future Work
References
All Topics
Computer Science
Natural Language Processing

Intelligent techniques for effective information retrieval

Profile image of TANVEER IRSHAD SIDDIQUITANVEER IRSHAD SIDDIQUI

2006, ACM SIGIR Forum

https://doi.org/10.1145/1189702.1189717
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188 pages

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Abstract

With the explosive growth of information, it is becoming increasingly difficult to retrieve the relevant documents with statistical means only. This begets new challenges to IR community and motivates researchers to look for intelligent Information Retrieval (IR) systems that search and/or filter information automatically based on some higher level of understanding are required. This higher level of understanding can only be achieved through processing of text based on semantics, which is not possible by considering a document as a "bag of words". We make a humble effort in this direction by investigating techniques that attempt to utilize semantics to improve effectiveness in IR. The hypothesis is that with an improved representation of documents and by incorporating limited semantic knowledge, it is possible to improve the effectiveness of an IR system.We propose the use of Conceptual Graph (CG) formalism for representing text. The level of semantic details to be capture...

Figures (52)
Figure 2.1: Basic Information Retrieval Process assumed relevant and returned to the user.
Figure 2.1: Basic Information Retrieval Process assumed relevant and returned to the user.
Table 2.1: Different types of referents Information Retrieval and Conceptual Graphs: Basic Concepts
Table 2.1: Different types of referents Information Retrieval and Conceptual Graphs: Basic Concepts
Figure 2.5: Conceptual graph of sentence (2.2) form of a CG representing the English sentence: Any statement expressed in any one of these notations can be automatically translated
Figure 2.5: Conceptual graph of sentence (2.2) form of a CG representing the English sentence: Any statement expressed in any one of these notations can be automatically translated
Figure2.6: Derivation graph of a conceptual graph v proceed in the same direction [2].
Figure2.6: Derivation graph of a conceptual graph v proceed in the same direction [2].
collections are presented.
collections are presented.
Figure 2.7: Recall-Precision graph them can not be avoided.
Figure 2.7: Recall-Precision graph them can not be avoided.
Table 3.1: Vector representation of Sample Documents after stemming Figure 3.1: Sample documents
Table 3.1: Vector representation of Sample Documents after stemming Figure 3.1: Sample documents
Figure 3.2: Results of application of indexing steps and the second triple correspond to the weighting strategy used for the query.
Figure 3.2: Results of application of indexing steps and the second triple correspond to the weighting strategy used for the query.
Table 3.2: Calculating weight with different options for the three weighting factors Chapter 3. Basic Framework: Document & query representation, matching & similarity Measure
Table 3.2: Calculating weight with different options for the three weighting factors Chapter 3. Basic Framework: Document & query representation, matching & similarity Measure
Figure 3.3: Steps in preprocessing documents document, in the collection, or some combination of both.
Figure 3.3: Steps in preprocessing documents document, in the collection, or some combination of both.
Figure 3.4: Representation in two-dimensional vector space and the query are represented as 6), 02 and 03 respectively. in this space using term weights as coordinates. The angles between these documents The simplest way of comparing document and query is by counting the number of document and the query have been shown. Documents dl, d2 and d3 are representec
Figure 3.4: Representation in two-dimensional vector space and the query are represented as 6), 02 and 03 respectively. in this space using term weights as coordinates. The angles between these documents The simplest way of comparing document and query is by counting the number of document and the query have been shown. Documents dl, d2 and d3 are representec
Figure 3.5: The meaning triangle [155] (Sowa, 1984) actually a formalized representation of the belief that people remember particular
Figure 3.5: The meaning triangle [155] (Sowa, 1984) actually a formalized representation of the belief that people remember particular
Figure 3.6: General Architecture of Conceptual graph Construction context and the knowledge base.
Figure 3.6: General Architecture of Conceptual graph Construction context and the knowledge base.
Table 3.3: Syntactic patterns for noun phrase Chapter 3 Basic Framework: Document & query representation, matching & similarity Measure
Table 3.3: Syntactic patterns for noun phrase Chapter 3 Basic Framework: Document & query representation, matching & similarity Measure
Figure 3.8: Sample Document text scientific information . The American chemical society has begun an analysis of the rele of the
Figure 3.8: Sample Document text scientific information . The American chemical society has begun an analysis of the rele of the
Figure 3.9: Conceptual graph for the sample document Document Representation and Similarity Measures
Figure 3.9: Conceptual graph for the sample document Document Representation and Similarity Measures
Figure 4.1: Retrieval Model Fig. 4.1 shows our retrieval model. Two models are used in conjunction.
Figure 4.1: Retrieval Model Fig. 4.1 shows our retrieval model. Two models are used in conjunction.
Table 4.1: Retrieval results of various models (Averaged over all the queries,
Table 4.1: Retrieval results of various models (Averaged over all the queries,
Techniques for Improving Effectiveness in Information Retrieval
Techniques for Improving Effectiveness in Information Retrieval
Figure 4.3: Recall-Precision curve for Model 1 vs. Model 3
Figure 4.3: Recall-Precision curve for Model 1 vs. Model 3
Figure 4.4: Recall-Precision curve for Model 1 vs. Model 4 Techniques for Improving Effectiveness in Information Retrieval
Figure 4.4: Recall-Precision curve for Model 1 vs. Model 4 Techniques for Improving Effectiveness in Information Retrieval
Figure 4.5: Recall-Precision curve for Model 1 vs. Model 5
Figure 4.5: Recall-Precision curve for Model 1 vs. Model 5
Figure 4.6: Comparison of Recall-precision behavior of model 6 with model 1 & 7 Techniques for Improving Effectiveness in Information Retrieval
Figure 4.6: Comparison of Recall-precision behavior of model 6 with model 1 & 7 Techniques for Improving Effectiveness in Information Retrieval
Figure 4.7: Comparison of seven different retrieval models
Figure 4.7: Comparison of seven different retrieval models
Figure 4.8: Recall-Precision curve for CACM collection (averaged over 64 queries) for BM25 scheme and “modified vector” model Techniques for Improving Effectiveness in Information Retrieval
Figure 4.8: Recall-Precision curve for CACM collection (averaged over 64 queries) for BM25 scheme and “modified vector” model Techniques for Improving Effectiveness in Information Retrieval
Figure. 4.9: Part of Concept hierarchy
Figure. 4.9: Part of Concept hierarchy
Figure 4.10: Intersection of conceptual graphs G1 and G2 These values have been set empirically.
Figure 4.10: Intersection of conceptual graphs G1 and G2 These values have been set empirically.
Techniques for Improving Effectiveness in Information Retrieval
Techniques for Improving Effectiveness in Information Retrieval
Figure 4.11: Recall-Precision curve for query 63 of the CACM-3204 collection Table 4.4: Percentage increase in precision
Figure 4.11: Recall-Precision curve for query 63 of the CACM-3204 collection Table 4.4: Percentage increase in precision
Figure 4.12: Recall-Precision curve for query 30 of the CACM-3204 collection Techniques for Improving Effectiveness in Information Retrieval
Figure 4.12: Recall-Precision curve for query 30 of the CACM-3204 collection Techniques for Improving Effectiveness in Information Retrieval
Figure 4.13: Recall-precision curve for CACM query 19
Figure 4.13: Recall-precision curve for CACM query 19
Figure 4.14: Average Recall-Precision curve for the subset of CACM queries Techniques for Improving Effectiveness in Information Retrieval
Figure 4.14: Average Recall-Precision curve for the subset of CACM queries Techniques for Improving Effectiveness in Information Retrieval
Figure 4.16: Ranks of documents after first and second stage onsideration for query | have been listed in fig.4.15
Figure 4.16: Ranks of documents after first and second stage onsideration for query | have been listed in fig.4.15
ranking have been achieved for other queries also. Figure 4.17: Ranking after first and second stage of retrieval for query 2 and 3. documents being considered can be found in Table 4.6. Similar improvements in
ranking have been achieved for other queries also. Figure 4.17: Ranking after first and second stage of retrieval for query 2 and 3. documents being considered can be found in Table 4.6. Similar improvements in
Figure 4.18: Ranking of first ten documents returned by LYCOS after second stage Techniques for Improving Effectiveness in Information Retrieval
Figure 4.18: Ranking of first ten documents returned by LYCOS after second stage Techniques for Improving Effectiveness in Information Retrieval
Table 5.1: Average 11AvgP score for a subset of CACM queries shows average recall precision curve for this comparison.
Table 5.1: Average 11AvgP score for a subset of CACM queries shows average recall precision curve for this comparison.
Figure 5.2. Average Recall-precision curves for a subset of CACM queries Automatic Query Expansion through Conceptual Graph
Figure 5.2. Average Recall-precision curves for a subset of CACM queries Automatic Query Expansion through Conceptual Graph
Figure 5.3: Average Recall-Precision curves for a subset of CACM queries for techniques (a), (b) and blind expansion
Figure 5.3: Average Recall-Precision curves for a subset of CACM queries for techniques (a), (b) and blind expansion
Figure 6.4: Recall-precision curve for CACM collection for vector model and our model using CGSim I Capturing Semantics through Relation Matching
Figure 6.4: Recall-precision curve for CACM collection for vector model and our model using CGSim I Capturing Semantics through Relation Matching
Figure 6.5: Recall-precision curve for CACM collection for vector model and our model using CGSim IT
Figure 6.5: Recall-precision curve for CACM collection for vector model and our model using CGSim IT
Table 6.2: Comparison of retrieval results for a subset of CACM queries
Table 6.2: Comparison of retrieval results for a subset of CACM queries
Figure 6.6: Recall-precision behavior of a subset of CACM queries with CGSim II Capturing Semantics through Relation Matching
Figure 6.6: Recall-precision behavior of a subset of CACM queries with CGSim II Capturing Semantics through Relation Matching
Figure 6.7: Recall-precision curve for CACM collection for vector model and our model using CGSim ITI Capturing Semantics through Relation Matching
Figure 6.7: Recall-precision curve for CACM collection for vector model and our model using CGSim ITI Capturing Semantics through Relation Matching
Figure 6.8: Recall-precision curve for CACM collection for vector model and our model using CGSim IV
Figure 6.8: Recall-precision curve for CACM collection for vector model and our model using CGSim IV
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References (194)

  1. J Allan, "Relevance feedback with too much data", Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retri-eval, pp. 337-343, 1995.
  2. G. Amati, and I. Ounis, "Conceptual Graphs and First Order Logic", The Computer Journal, 43(1), 2000.
  3. G. Amati, and F. Crestani, "Probabilistic learning by uncertainty sampling with non-binary relevance". In Fabio Crestani and Gabriella Pasi(Eds.), Soft computing in Information Retrieval: Techniques and Applications, Physica- Verlag, p.292-313, 2000.
  4. R. Baeza-Yates, B. Ribeiro-Neto, "Modern information retrieval". ACM Press, Addison-Wesley, 1999.
  5. K. Barker and N. Cornacchia, "Using noun phrase heads to extract document key phrases" In: H.J. Hamilton (Ed.) Advances in Artificial Intelligence. Proceedings of 13th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2000. Montreal, Berlin: Springer- Verlag, May 2000.
  6. M. Beaulieu, S. Robertson, & E. Rasmussen. "Evaluating interactive systems in TREC". Journal of the American Society for Information Science, 47(1), 85- 94,1996.
  7. D. E. Rose and R. K. Belew. "A connectionist and symbolic hybrid for improving legal research". Int. Journal of Man-Machine Studies, 35(1):1-33, 1991.
  8. N. J. Belkin, C. Cool, D. Kelly, S. J. Lin, Park, S. Y., J. Perez-Carballo, and C. Sikova "Iterative exploration, design and evaluation of support for query reformulation in interactive information retrieval". Information Processing and Management, 37, 403-434, 2001.
  9. D. Blair, and M. Maron, "Full text information Retrieval: Further analysis and classification". Information Processing and Management, 26(3): 437-477, 1990.
  10. A. Bookstein, "The anonymous behavior of precision in the Swets model, and its resolution". Journal of Documentation, 21, 374-380, 1974.
  11. James D. Anderson, Jose Perez-Carballo, "The nature of indexing: how humans and machines analyze messages and texts for retrieval. Part II: Machine indexing, and the allocation of human versus machine effort", Information Processing and Management, 37, 255-277, 2001.
  12. Bong-Hyun Cho, Changki Lee, Gary Geunbae Lee, "Exploring term dependences in probabilistic information retrieval model", Information Processing and Management 39, 505-519, 2003.
  13. Anastasios Tombros, C.J. van Rijsbergen. "Query-sensitive similarity measures for information retrieval". Knowledge and Information Systems, 6, 617-642, 2004.
  14. P. Borlund and P. Ingwersen, "The development of a method for the evaluation of interactive information retrieval systems". Journal of Documentation, 53:225-250, 1997.
  15. P. Borlund. "Experimental components for the evaluation of interactive information retrieval systems". Journal of Documentation, Vol. 56, No. 1, 2000.
  16. P. Borlund, "The IIR evaluation model: a framework for evaluation of interactive information retrieval systems". Information research, 8(3), 2003.
  17. M. Boughanem, C. Chrisment, J. Mothe, C. Soul-Dupuy, and L. Tamine, "Connectionist and Genetic Approaches for Information Retrieval". In Febrio Crestani and Gabriella Pasi(Eds.), Soft Computing in Information Retrieval: Techniques and Applications, Physica-Verlag, pp. 173-198, 2000.
  18. J. Broglio, J. P. Callan, W. B. Croft, & D. W. Nachbar, "Document retrieval and routing using the INQUERY system". In Proceedings of the Third Text REtrieval Conference (TREC-3), NIST Special Publication (500-225), pp. 29- 38, 1995
  19. M. Buckland, and F. Gey, "The relationship between recall and precision". Journal of the American society for the information science, 45(W1):12-19, 1994.
  20. C. Buckley, J. Allan, G. Salton and A. Singhal, "Automatic routing and retrieval using SMART: TREC-2". Information Processing & Management, 31(3), pp. 315-326,1995.
  21. W. R. Caid, Susan T. Dumais, and S. I. Gallant, "Learned vector space model for document retrieval". Information Processing & management, vol. 31(3), pp. 419-429, 1995.
  22. J P Callan, W B Croft, S. M. Harding. "The INQUERY retrieval system". In : Proceedings of the third International conference on Database and Expert system's Applications, Valencia, Spain, pp. 78-83, 1992.
  23. J.P. Callan, "Passage-level evidence in document retrieval", Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 302-310, 1994.
  24. Michael Chein, et Marie-Laure Mugnier, "Conceptual graphs: fundamental notions", Revue d'Intelligence Artificielle, 6(4), pp 365-406, 1992.
  25. Michael Chein et Marie-Laure Mugnier, "Conceptual graphs are also graph", Research Report, 95-004, LIRMM, 1995.
  26. H Chen, "Machine learning for Information Retrieval: Neural networks, Machine learning and Genetic Algorithms". Journal of the American Society for Information Science: 46(3): 194-216, 1995.
  27. Fangfang Feng, W. Bruce Croft, "Probabilistic techniques for phrase extraction" Information Processing and Management, 37, 199-220, 2001.
  28. C. W. Cleverdon, "On the inverse relationship between recall and precision". Journal of Documentation, 28, 195-201, 1972.
  29. W. W. Cohen, and Y Singer, "Context-sensitive learning methods for text categorization". In Proceedings of the 19 th Annual ACM SIGIR conference, 1996.
  30. D. B. Lenat and R. V. Guha, "Building large knowledge bases". Addison-Wesley, Reading, MA, 1990
  31. Edwin M. Cortez, Sang C. Park, and Seonghee Kim, "The hybrid application of an inductive learning method and a neural network for intelligent information retrieval". Information Processing & Management, Vol. 31, No. 6, pp. 789- 813,1995.
  32. F. Crestani, M. Lemas, C. J. V. Rijsbergen, and I. Campbell, "Is the document relevant? …Probably": a survey of probabilistic models in Information retrieval". ACM Commuting surveys, 30(4), 528-552, 1998.
  33. W. B. Croft, and D. J. Harper, "Using probabilistic model of document retrieval without relevance information". Documentation, 35(4), 285-295, 1979.
  34. W. B. Croft, "Experiments with representation in a document retrieval system". Information Technology: Research and Development, 2(1), 1-21, 1983.
  35. W. B. Croft, H. R. Turtle & D. D. Lewis. "The Use of Phrases and Structured Queries in Information Retrieval". In A. Bookstein, Y. Chiaramella, G. Salton, & V. V. Raghavan (Eds.), SIGIR '91: Proceedings of the Fourteenth Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, New York: ACM Press, pp. 32-45, 1991.
  36. S. Deerwester, Susan T Dumais, George W Furnas, and Thomas K. Landauer, "Indexing by Latent Semantic Analysis". Journal of the American Society of Information Science, 1990.
  37. Judith P Dick, "A conceptual case relation representation of text for intelligent retrieval", Technical Report. CSRI-265, 1992.
  38. Darina Dicheva and Vania Dimitrova. "An approach to representation and extraction of terminological knowledge in ICALL". Journal of Computing and Information Technology CIT, vol. 6, No. 1, 1998, pp.39-52.
  39. Sander Dominich, "Mathematical foundation of Information Retrieval". In R. Lowen (Ed.) Mathematical modeling: Theory and applications, Kluwer Academic Pub., 2001.
  40. S. W. Draper, and M. D. Dunlop. "New IR-New Evaluation: The impact of interaction and multimedia on information retrieval and its evaluation". The new review of hypermedia and multimedia, 3:107-12, 1997.
  41. M. Eisenberg, C. Barry, "Order effects: a study of the possible influence of presentation order on user judgments of document relevance". Journal of the American Society for Information Science, 39:293-300, 1988.
  42. D. Ellis. "The Dilemma of Measurement in Information Retrieval". Journal of the American Society for Information Science, 47(1), 23-36, 1996.
  43. David A. Evans and C Zhai. "Noun phrase analysis in unrestricted text for information retrieval". Computational Linguistics, 1996.
  44. J. L. Fagan, "The effectiveness of a nonsyntactic approach to automatic phrase indexing for document retrieval". Journal of the American Society for Information Science, 40(2), 115-132, 1989.
  45. G. Salton, and C. Buckley, (1990). Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science, 51(4), 288-297
  46. Peter W Foltz, "Using Latent Semantic Indexing for Information Filtering". The ACM conference on office information system (COSIS'90), 1990. (Lecture notes in Artificial Intelligence, vol. 954), Santa Cruz, CA, Springer- Verlag, pp.158-172, 1995.
  47. M. Gordon and M. Kochen, "Recall-Precision trade-off: A derivation". Journal of the American Society for Information Science, 40, 145-151, 1989.
  48. M. Gordon, "Probabilistic and Genetic algorithm for Document retrieval". Communications of the ACM. 31(10): 1208-1218, 1988.
  49. Daniel Gildea and Daniel Jufrasky, "Automatic labeling of semantic roles" Computational linguistics, 28(3), 245-288, 2002
  50. D. Harman. "Relevance feedback revisited". Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.1-10, 1992.
  51. Harter S. P. "Psychological relevance and information science". Journal of the American Society for Information Science, 43, 602-615, 1992.
  52. A Hearst and J O Pedersen. "Reexamining the cluster hypothesis: Scatter/Gather on retrieval results" In proceedings of the 19 th ACM/SIGIR conference on R & D in IR (SIGIR'96) 1996, pp.76-84.
  53. R. B. Hayenes, K. A. McKibbon, Walker C. J. "Online access to MEDLINE in clinical settings". Annals of Internal Medicine, 1990;112(1):78-84, 1990.
  54. E. Voorhees, "Query expansion using lexical-semantic relations". In B. Croft, & C. van Rijsbergen, Proceedings of the 17th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland,p. 61-69). London, UK: Springer-Verlag 3-6 July, 1994.
  55. W. R. Hersh, D. H. Hickam, "A performance and failure analysis of SAPHIRE with a MEDLINE test collection". Journal of the American Medical Informatics Associatio,1:51-60, 1994.
  56. W. R. Hersh, D. L. Elliot, D. H. Hickman, , Stephanie L Wolf, Anna Molnar, Christine Leichtenstien. "Towards New Measures of Information Retrieval Evaluation", SIG IR'95, 1995.
  57. D. Hiemstra, Arjen P. de Vries. "Relating the new language models of information retrieval models", CTIT technical report TR-CTIT-00-09, May 2000, http://www.ctit.utwente.nl
  58. X. J. Huang, and S. E. Robertson, "Applications of probabilistic methods to Chinese text retrieval". Journal of documentation. 53:74-79, 1997.
  59. E. Ide. "New experiments in relevance feedback", In G. Salton(Ed.), The SMART retrieval system experiments in automatic document processing (Chapter 16) Englewood Cliffs, NJ, Prentice hall, 1971
  60. B. J. Jansen, A. Spink and T. Saracevic, 'Real life, real user and real needs: A study and analysis of user queries on the Web". Information Processing & Management, 36(2), 207-227, 2000.
  61. K. Järvelin and J. Kekäläinen "IR evaluation methods for retrieving highly relevant document". In: Belkin, N.J., Ingwersen, P. & Leong, M-K., eds. Proceedings of the 23rd ACM SIGIR Conference on Research and Development of Information Retrieval, Athens, Greece, 2000. New York, N.Y.: ACM Press, pp. 41-48, 2000.
  62. Y. Jing, and W. B. Croft, "An association thesaurus for information retrieval, Technical report". UMASS-CS-94-11, University of Massachusetts, 1994.
  63. D. Jufrasky, and J. H. Martin. "Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition". Pearson education, Asia, 2000.
  64. C. Khoo, "The Use of Relation Matching in Information Retrieval", LIBRES: Library and Information Science Research, 7(2), 1997.
  65. V. Kulyukin. "Application-Embedded retrieval for distributed from text collections". Proceedings of AAAI-99, 1999.
  66. Christopher S. G. Khoo, S. H. Myaeng, R. N. Oddy, "Using cause-effect relations in text to improve information retrieval precision", Information Processing and Management, 37, 119-145, 2001.
  67. Kim, Sang-Bum, Seo, Hee-Cheol and Rim, Hac-Chang, "Information Retrieval using word senses: Root sense tagging approach", SIGIR'04, Sheffield, UK, 2004.
  68. J. H. Lee. "Analyzing the effectiveness of extended Boolean models in information retrieval". Technical report TR95-1507, Cornell University, 1995. http://cs.tr.cs.cornell.edu/
  69. J. H. Lee. "Combining multiple evidence from different properties of weighting schemes', Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 180- 188, 1995.
  70. R. Levinson, and G. Ellis. "Multi-level Hierarchical retrieval". Knowledge- Based Systems, 5(3), 1992.
  71. D.D. Lewis, "An evaluation of phrasal and clustered representations on a text categorization task", Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 37-50, 1992.
  72. D. D. Lewis and K. S. Jones, "Natural language processing for information retrieval". ACM, 39(1), 92-101,1996.
  73. E. D. Liddy, S. H. Myaeng, "DR-LINK: a system update for TREC-2". Second Text REtrieval Conference, (NIST-SP 500-215). NIST. pp.85-99. Washington, DC, USA, 1994.
  74. G. Z. Liu, "Semantic vector space model: Implementation and evaluation". Journal of the American Society of Information Science, 48(5), pp.395-417, 1997.
  75. Shuang Liu, Fang Liu, Clement Yu and Weiyi Meng. "An effective approach to document retrieval via utilizing WordNet and recognizing phrases", 2004.
  76. Robert M Losee. "Comparing boolean and probabilistic information retrieval systems across queries and disciplines". Journal of the American Society for Information Science, 48(2), pp.143-156, 1997.
  77. H. P. Luhn, "The automatic creation of literature abstracts". IBM journal of Research and Development, 2(2), 1958.
  78. H. P. Luhn, "A statistical approach to mechanized encoding and searching of literary information". IBM Journal of Research and development. 1(4), 309-317, 1957.
  79. R. Marega, M. T. Pazienza, "CoDHIR: an information retrieval system based on semantic document representation". Journal of Information Science, 20(6), pp.399-412. UK, 1994.
  80. M. E. Maron, and J. L. Kuhns, "On relevance, probabilistic indexing and information retrieval". Association for computing machinery, 7(3) 216-244, 1960.
  81. P. Martin and P. Eklund, "Embedding knowledge in Web documents", Griffith University,Australia, http://decweb.ethz.ch/WWW8/data/2145/html/bindex.htm, April 24, 2000.
  82. P. Martin. "CGKAT: A knowledge acquisition and retrieval tool using structured documents and ontologies". In: Lukose D, Delugach H, Keeler M, Searle L, Sowa JF (eds). Proceedings of ICCS'97 (Lecture notes in artificial intelligence 1257). Springer, Berlin Heidelberg New York, pp 581-584, 1997.
  83. Victoria McCargar, "Statistical approaches to automatic text summarization". Bulletin of the American Society for Information Science and Technology, Vol. 30, No. 4, April/May 2004.
  84. C. T. Meadow, "Text Information Retrieval Systems". Academic Press: San Diego, 1992.
  85. G. Miller. "WordNet: An Online lexical database". International journal of lexicography, 24: 513-523, 1990.
  86. G. Miller, "WordNet: A lexical database for English". Communication of the ACM, 38(11), 1995.
  87. E. Mittendorf, B. Mateev and P. Schäuble, "Using the co-occurrence of words for retrieval weighting". Information Retrieval, 3, 243-251, 2000.
  88. M. Mitra, C. Buckley, A. Singhal, and C. Cardic. "An analysis of statistical and syntactic phrases". In L. Devroye and C. Chrisemnet, Conference Proceedings of RIAO-97, Centre de Hautes Etudes Internationales d' Informatique Documentaires, pp. 200-214, 1997.
  89. S. Mizzaro, "How many relevance's in information retrieval?". Interacting with Computers. 10(3). pp.305-322, 1998.
  90. S. Mizzaro, "Relevance: the whole history". Journal of the American Society for Information Science. 48(9), pp. 810-832, 1997.
  91. M. Montes-y-Gómez, A. López-López, and A. Gelbukh, "Comparison of conceptual graphs", Cario O, Sucar L E, Cantu FJ (Eds.) MICAI 2000: Advances in Artificial Intelligence. Lecture notes in Springer -Verlag, pp. 548- 556, 2000.
  92. M. Montes-y-Gómez, A. Gelbukh, A López-López and Baeza-Yates, "Flexible Comparison of Conceptual Graphs". Lecture notes in Computer Science 2113, Springer-Verlag, 2001.
  93. M. L. Mugnier, "On Generalization/Specialization for Conceptual Graphs". Journal of Experimental and theoretical Artificial Intelligence, vol. 7, pp.325- 344, 1995.
  94. S. H. Myaeng, and E. D. Liddy, "Information retrieval with semantic representation of texts". In Proceedings of the 2nd Annual Symposium on Document Analysis and Information Retrieval, pp. 201-215, 1993.
  95. Sung H Myaeng and Aurelio Lopez-Lopez, "Conceptual graph matching: a flexible algorithm and experiments". Journal of Experimental and Theoretical Artificial Intelligence, vol.4, 1992.
  96. Natalya Fridman Noy. "Knowledge Representation for Intelligent Information Retrieval in experimental sciences". Ph. D. thesis, Northheastern University, Bostan, MA, 1997.
  97. Nir Oren, "Reexamining tf.idf based information retrieval with Genetic Programming". Proceedings of SAICIST 2002, p.224-234, 2002.
  98. Iadh Ounis, Marius Pasca. "RELIEF: Combining expressiveness and rapidity into a single system". SIGIR'98, Melbourn, Australia, 1998.
  99. C. P. Paice. "Soft evaluation of Boolean search queries in information retrieval systems". Information technology: Research and development, 3(1):33-42, 1984.
  100. H. S. Park and Sangwolgok-Dong, "Inferential representation of science documents". Information Processing and Management, 32(4), 419-429, 1995.
  101. J. Perez-Carballo, and T. Strzalkowski, "Natural language processing: progress report". Information Processing and Management, 36, 155-178, 2000.
  102. Justin Picard and Jacques Savoy, "A logical Information Retrieval Model based on a combination of propositional logic and probability theory". In Fabio Crestani, Gabriella Pasi (Eds.) Soft Computing in Information Retrieval: Techniques and applications, pp. 225-228, 2000.
  103. M. F. Porter, "An algorithm for suffix stripping. Program", 14(3), 130-137, 1980.
  104. V. V. Raghavan, H. P. Shi, and C. T. Yu, "Evaluation of the 2-poisson model as a basis for using term frequency data in searching". Sixth international conference on research and development in information retrieval. Bethesda, Maryland, 1983.
  105. D. V. Rama, and P. Srinivasan, "An investigation of content representation using text grammars", ACM transactions on Information Systems, 11(1) pp. 51- 75, 1993.
  106. L. Rau, "Exploring the semantics of conceptual graphs for efficient graph matching". In Proceedings of the 3 rd Annual Workshop on conceptual graphs, Boston, 1988.
  107. Jane Reid, "A Task-Oriented Non-Interactive Evaluation Methodology for Information Retrieval Systems", Information Retrieval, 2, 115-129, 2000.
  108. Robertson et al. "Okapi at TREC-2". In the second Text Retrieval Conference(TREC-2) edited by D K Harman, Gaithersburg, MD: NIST, 1994.
  109. S. Robertson, S. Walker, S. Jones, M. Hancock-Beaulieu and M. Gatford, "Okapi at TREC-3". In: Harman D (Ed.), The Third Text REtrieval Conference (TREC-3). NIST Special Publication 500-225, 1995.
  110. S. E. Robertson and K. S. Jones, "Simple proven approaches to text retrieval", 1997. http://www.n3labs.com/ pdf /robertson97simple.pdf
  111. S. E. Robertson, S. Walker. "On Relevance Weights with Little Relevance Information", Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 16-24, 1997.
  112. Ana-Maria Giuglea and Alessandro Moschitti "Knowledge Discovering using FrameNet, VerbNet and PropBank". http://ai-nlp.info.uniroma2.it/moschitti /articles/ECML04_G.pdf.
  113. S. E. Robertson, and Sparck Jones. "Relevance weighting of search terms". Journal of American Society for Information Science, 27: 129-146, 1976.
  114. S. E. Robertson, and S. Walker, "Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval". In Proceedings of the Seventeenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Springer-Verlag, New York, 232-241, 1994.
  115. IEEE Standard Upper Ontology web site: http://suo.ieee.org , IEEE, 2001
  116. S. E. Robertson, M -E Maron, and W. S. Cooper, "Probability of Relevance: A unification of two competing models for document retrieval". Information Technology: research and Development. 1:1-21,1982.
  117. J. J. Rocchio. "Relevance feedback in information retrieval". In: G Salton(ed.) The SMART retrieval system: Experiments in automatic document processing (Chapter 14) Englewood Cliffs, NJ: Prentice-Hall,1971.
  118. Ian Ruthven and Mounia Lalmas(no date). A survey on the use of relevance feedback for information access systems. http://www.cis.strath.ac.uk/research /publications/papers/strath_cis_publication_135.pdf
  119. Richard Osdin, IAdh Ounis and Ryen W. White, "Using hierarchical clustering and summarization approaches for Web retrieval: Glassgow at TREC-2002 International track", http://trec.nist.gov/pubs/trec11/t11_proceedings.html, 2002
  120. G. Salton and C. Buckley, "Term Weighting approaches in automatic text retrieval". Information Processing and Management, 24(5), 513-523, 1988.
  121. G. Salton, "The SMART Retrieval system -Experiments in Automatic document processing". Englewood Cliffs, NJ Prentice Hall, 1971.
  122. G. Salton and C. S. Yang. "On the specification of term values in automatic indexing". Journal of Documentation. 29(4) 351-372, 1973.
  123. G. Salton, and M. J. McGill, "Introduction to modern information retrieval". New York: McGraw-Hill, 1983.
  124. C. Buckley, J.Allan, G. Salton,.
  125. & A. Singhal, "Automatic query expansion using SMART: TREC-3". In Proceedings of the Third Text REtrieval Conference (TREC-3), NIST Special Publication (500-225), 69-80, 1995.
  126. G. Salton, E. A. Fox, and H. Wu, "Extended Boolean information retrieval". Communication of the ACM; 26(11), 1022-026, 1983.
  127. G. Salton, J. Allan, C. Buckley, "Approaches to Passage Retrieval in Full Text Information Systems", Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 49-58, 1993.
  128. G. Salton, C. Buckley and M. Smith. "On the application of syntactic methodologies in automatic text analysis". Information Processing & Management, 26, 73-92, 1990.
  129. M. Sanderson, "Word Sense disambiguation in IR". ACM SIGIR, 1994.
  130. M. Sanderson, "Retrieving with good sense". Information Retrieval, 2(1):49-69, 2000.
  131. W. M. Shaw Jr, Robert Burgin and Patrick Howell, "Performance Standards and Evaluations In IR Test Collections: Cluster-Based Retrieval Models" Information Processing & Management, Vol. 33, No. I, pp. 1-14, 1997
  132. T. Saracevic, "Evaluation of evaluation in information retrieval". In: Fox, E.A., Ingwersen, P., & Fidel, R., eds. Proceedings of the 18th ACM Sigir Conference on Research and Development of Information Retrieval. Seattle, 1995. N.Y.: ACM Press, pp. 138-146, 1995.
  133. T. Saracevic, "Relevance reconsidered". In Ingwersen P and Pors N. O. (eds.) Proceedings of CoLIS 2, Second International Conference on Conceptions of Library and Information Science: Integration in Perspective. The Royal School of Librarianship, Copenhagen, pp. 201-218, 1996.
  134. T. Saracevic, "Individual differences in organizing, searching and retrieving information". In Proceedings of the 54th Annual Meeting of the American Society for Information Science (ASIS). pp. 82-86, 1991.
  135. J. Savoy, and J. Picard. "Retrieval effectiveness on the web". Information Processing & Management, 37(4), 543-569, 2001.
  136. L Schamber, M. B. Eisenberg, M. S. Nilan, "A reexamination of relevance: toward a dynamic, situational definition". Information Processing and Management, 1990.
  137. H. Schutz, and J O Pedersen, "Information retrieval based on Word senses". Proceedings of 4 th Annual symposium on document analysis and information retrieval, pages 161-175, 1995.
  138. S. B. Shum, E. Motta, John Domingue, "ScholOnto: an ontology-based digital library server for research documents and discourse". Int. Journal of Digital Library, 3, pp. 237-248, 2000.
  139. A. Singhal, G. Salton, M. Mitra, and C. Buckley, "Document Length normalization". Information Processing & Management, Vol. 32, No. 5, pp. 619-633, 1996.
  140. Andrew J Slade, and Albert F. Bokma. "Conceptual Approaches for Personal and Corporate Information and Knowledge Management". Proceedings of the 34th Hawaii International Conference on System Sciences -2001.
  141. Alan F Smeaton, "Natural Language Processing and information retrieval". A tutorial presented at EACL'95 .
  142. A. F. Smeaton and C. J. van Rijsbergen. "Experiments on incorporating syntactic processing of user queries into a document retrieval strategy". In Y. Chiaramella (Ed.), 11th International Conference on Research & Development in Information Retrieval. New York: ACM, pp. 31-51, 1988.
  143. A.F. Smeaton. "Using natural language processing or natural language processing resources for information retrieval". In (Strzalkowsi, T.) : Natural Language Information Retrieval, Kluwer, pp. 99-112,1999.
  144. A.F. Smeaton, R. O'Donnell, & F. Kelledy. "Indexing structures derived from syntax in TREC-3: System description". In D.K. Harman (Ed.), Overview of the Third Text REtrieval Conference (TREC-3) NIST Special Publication 500-225, Gaithersburg, MD: National Institute of Standards and Technology, pp. 55-67, 1995.
  145. J. F. Sowa. "Conceptual structures: Information processing in mind and machine", Addison -Wesley 1984.
  146. J. F. Sowa. "Knowledge Representation". Brooks/Cole publishing, CA, USA, 2000.
  147. J. F. Sowa, "Notation of conceptual graphs," Proceedings, Third Annual Workshop on conceptual graphs. Menlo Park, CA., August 17, 1988.
  148. J. F. Sowa. "Relating diagram to logic". In Eds. Guy W. Mineau, Bernard Moulin and John F. Sowa, Proceedings of first International conference on conceptual structures, ICCS'93, Quebec city, Canada, August 4-7,1993.
  149. K. Sparck Jones, "Index term weighting". Information Storage and Retrieval, 9(11), 619-633, 1973.
  150. K. Sparck Jones, "Summary performance comparisons TREC-2, TREC-3, TREC-4, TREC-5". In TREC-5 Proceedings. Available: http://www- nlpir.nist.gov/TREC/trec5.papers/ sparckjones.ps, February 1997.
  151. K. Sparck-Jones, "A Statistical interpretation of term specificity and its application in retrieval", Journal of Documentation, 28, 111-121, 1972.
  152. K. Sparck Jones. "The role of artificial intelligence in information retrieval" Journal of the American Society for Information Science, 42(8):558-565, September 1991.
  153. C. Stokoe, M. P. Oakes, and J. Tait, "Word Sense disambiguation in information retrieval revisited". In proceedings of ACM SIGIR Conference (26) pp.159-166, 2003.
  154. T. Strzalkowski, "Natural Language Information Retrieval". Information Processing and Management, 31(3). 397-417, 1995.
  155. Nicholas J. Belkin, "Intelligent Information Retrieval: Whose Intelligence?" http://www.scils.rutgers.edu/~belkin/articles/isi96-papers.pdf [165].
  156. L. T. Su, "The relevance of recall and precision in user evaluation". Journal of the American Society of Information Science, 45:207-217, 1994.
  157. Hensman Svetlana, "Construction of conceptual graphs representation of texts". In proceedings of the student research workshop at HLT-NACCL, Bostan, USA, 49-54, 2004.
  158. J. A. Swets. "Effectiveness of Information Retrieval methods." American Documentation, 10, p.72-89,1969.
  159. A. Tombros. "The effectiveness of query-based hierarchic clustering of documents for information retrieval". PhD. thesis, University of Glasgow, 2002.
  160. M. Tuttle, D. McGuiness, "Report of the ontologies and ontology maintenance working group". Proceedings of the international Semantic Web Working Symposium, July 30-Aug. 1, Stanford University, California, USA, 2001. http://www.semanticweb.org/SWWS/report/
  161. C. J. van Rijsbergen "Information Retrieval". 2nd ed. Butterworths, London, 1979.
  162. C. J. van Rijsbergen, "Probabilistic retrieval revisited". The Computer Journal, 35(3): 291-298, 1992.
  163. E. M. Voorhees, "Using WordNet TM to disambiguate word sense for text retrieval". Proceedings of ACM SIGIR Conference(16), 171-180, 1993.
  164. E. M. Voorhees, "The philosophy of Information Retrieval evaluation", Lecture Notes in Computer science, pp.355-370, Springer-Verlag, 2001.
  165. E. M. Voorhees. "Natural language and information retrieval". LNCS, Springer- Verlag, 1999.
  166. E. M. Voorhees, D. Harman, "Overview of the Seventh Text REtrieval Conference (TREC-7)". In Proceedings of the 7th Text REtrieval Conference. NIST special publication, 500-242, pp. 1-23, 1999.
  167. D. Vrajitoru, "Crossover Improvement for the Genetic Algorithm in Information Retrieval". Information Processing and Management. 34(4): 405- 415, 1998.
  168. P. Wallis, "Information retrieval based on paraphrase". In proceedings of the Ist pacific association for computational linguistic conference, 1993.
  169. I. Ounis and T.W.C. Huibers. "A logical relational approach for information retrieval indexing". In 19th Annual BCS-IRSG Colloquium onIR Research, Aberdeen, Scotland. EWIC, Springer-Verlag, 8-9 April1997.
  170. M. Wermelinger, "Conceptual graphs and first order logic". In Ellis, G, Levinson, R, Rich and Sowa, JF (eds.) Proceedings of 3 rd International conference on conceptual structures, ICCS'95, (Lecture notes in Artificial Intelligence, vol. 954) Santa Cruz, CA, Springer-Verlag, pp.323-337, 1995.
  171. R. Wilkinson, "Effective Retrieval of Structured Documents". Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.311-317, 1994.
  172. Y -F Wu, "Automatic Concept organization: Organizing concepts from text through probability of co-occurrence analysis", SIGCR2000, 2000.
  173. Y. Xu, M. Benaroch, "Information retrieval with a hybrid automatic query expansion and data fusion procedure". Information Retrieval. 7, 2004 1-25.
  174. Gi-Chul Yang, and A. Burger, "An efficient storage and retrieval system for conceptual graphs", (no date).
  175. Gi-Chul Yang, Y Choi and J Oh, "CGMA: A novel conceptual graph matching algorithm", Conceptual structures: theory and implementation, Eds. Pfeiffer, T G E Nagle, New York, (LNAI 754), 1993.
  176. Lei Zhang, and Y. Yu, "Learning to generate CGs from domain specific sentences". In proceedings of 9 th international conference on conceptual structures, LNAI Vol. 2120, Springer-Verlag, 2001.
  177. Stephane Zrehen, "A connectionist approach to Content Access in documents: Application to Detection of Jokes". In Febrio Crestani and Gabriella Pasi(Eds.), Soft Computing in Information Retrieval, Physica-Verlag, pp.141- 169, 2000.
  178. Tanveer J Siddiqui and Uma Shanker Tiwary, "Integrating Relation and keyword matching in information retrieval". In Rajiv Kosla, Rober J. Howlett, Lakhmi C. Jain(Eds.). Proceedings of 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems: Data mining and soft computing applications-II, Lecture Notes in Computer Science, vol. 3684, pages 64-71, Melbourne, Australia, September, 2005. Springer Verlag.
  179. Tanveer J Siddiqui and Uma Shanker Tiwary, "Integrating notion of agency and semantics in information retrieval: An intelligent multi-agent information retrieval model". In proceedings of International Conference on Intelligent Systems Design and Applications (ISDA 2005), September, 2005, Wroclaw, Poland, IEEE Computer Society.
  180. Tanveer J Siddiqui, and Uma Shanker Tiwary, "A hybrid model to improve relevance in document retrieval", Accepted (October 16, 2005) for publication in Journal of Digital Information Management, Digital Information Research Foundation.
  181. Tanveer J Siddiqui, and Uma Shanker Tiwary, "Automatic query expansion through conceptual graphs", Presented in International Conference on Cognitive Science (ICCS), Allahabad, India, December 16-18, 2004.
  182. Tanveer J Siddiqui, and Uma Shanker Tiwary, "A WordNet based query reformulation technique to improve retrieval effectiveness", National Seminar on Internet: New Horizons, J.K. Institute of Applied Physics & Technology, University of Allahabad, Allahabad, India, February 13-14, 2005.
  183. Tanveer J Sidddiqui and Uma Shanker Tiwary, "Text Summarization and Conceptual Graphs" Presented in Document Summarization Workshop, IIIT, Allahabad, India, March19-20, 2005.
  184. SITUATION] (DSCR) [PROPOSITION:[CAT] (ON) [MAT]].
  185. EXPR (Experiencer). "links an state to an animate who is experiencing that state." MNR (Manner). "links an [ACT] to an [ATTRIBUTE].
  186. Ex.: Shereen drives slowly.
  187. MEAS (Measure). "links a [DIMENSION] to a [MEASURE] of that dimension. Ex.. The road is 10 km long. [ROAD] (CHRC) [LENGTH] (MEAS) [MEASURE:10 KM]
  188. OBJ(Object): "links an [ACT] to an [ENTITY], which is acted upon." Ex.: The cat swallowed the canary. [CAT:#] (AGNT) [SWALLOW] (OBJ) [CANARY:#] [154] PART: "links an [ENTITY:*x] to an [ENTITY:*y] where *y is part of *x". PAST: "is a monadic relation that links to a [PROPOSITION] that was true at some time preceding the present. Ex. Zuha left. (PAST) [PROPOSITION: [PERSON: ZUHA] (AGNT) [LEAVE]]
  189. PTNT (Patient): "links an [ACT] to an [ANIMATE]".
  190. QTY. Links a set of [ENTITY: {*}] to a [NUMBER] that indicates the number of entities in that set.
  191. Ex.: There are 10 students in the class. [CLASS] (LOC) [STUDENT: {*}] (QTY) [NUMBER: 50]
  192. RCPT (Recipient). "links an [ACT] to an [ANIMATE] which receives the object or result of an action".
  193. RSLT(Result). "links an [ACT] to an [ENTITY] that is generated by the act. Ex.: Arjun built a house. [PERSON: Arjun] (AGNT) [BUILD] [RSLT] [HOUSE]
  194. SUBT(Subtype). "links a type to subtype its subtype". Ex.: Elephant is a subtype of animal. [TYPE: ELEPPHANT] (SUBT) [TYPE: ANIMAL]

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Related topics

  • Information Retrieval
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