Advertisement

Identifying Semantic Relations in Text for Information Retrieval and Information Extraction

  • Christopher Khoo
  • Sung Hyon Myaeng
Part of the Information Science and Knowledge Management book series (ISKM, volume 3)

Abstract

Automatic identification of semantic relations in text is a difficult problem, but is important for many applications. It has been used for relation matching in information retrieval to retrieve documents that contain not only the concepts but also the relations between concepts specified in the user’s query. It is an integral part of information extraction—extracting from natural language text, facts or pieces of information related to a particular event or topic. Other potential applications are in the construction of relational thesauri (semantic networks of related concepts) and other kinds of knowledge bases, and in natural language processing applications such as machine translation and computer comprehension of text. This chapter examines the main methods used for identifying semantic relations automatically and their application in information retrieval and information extraction.

Keywords

Information Retrieval Noun Phrase Semantic Relation Information Extraction Parse Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Austin, D. (1984). PRECIS: A Manual of Concept Analysis and Subject Indexing (2nd ed.). London: British Library, Bibliographic Services Division.Google Scholar
  2. Berrut, C. (1990). Indexing medical reports: The RIME approach. Information Processing & Management, 26, 93–109.CrossRefGoogle Scholar
  3. Cardie, C. (1997). Empirical methods in information extraction. AI Magazine, 18(4), 65–79.Google Scholar
  4. Cowie, J., & Lehnert, W. (1996). Information extraction. Communications of the ACM, 39(1), 80–91.CrossRefGoogle Scholar
  5. Croft, W. B. (1986). Boolean queries and term dependencies in probabilistic retrieval models. Journal of the American Society for Information Science, 37, 71–77.Google Scholar
  6. Croft, W. B., Turtle, H. R., & Lewis, D. (1991). The use of phrases and structured queries in information retrieval. Proceedings of the Fourteenth Annuallnternational ACM/SIGIR Conference on Research and Development in Information Retrieval, 32–45.Google Scholar
  7. Dillon, M., & Gray, A. S. (1983). FASIT: A fully automatic syntactically based indexing system. Journal of the American Society for Information Science, 34, 99–108.CrossRefGoogle Scholar
  8. Fagan, J. L. (1989). The effectiveness of a nonsyntactic approach to automatic phrase indexing for document retrieval. Journal of the American Society for Information Science, 40, 115–132CrossRefGoogle Scholar
  9. Farradane, J. E. L. (1950). A scientific theory of classification and indexing and its practical applications. Journal of Documentation, 6, 83–99.Google Scholar
  10. Farradane, J. E. L. (1952). A scientific theory of classification and indexing: Further considerations. Journal of Documentation, 8, 73–92.Google Scholar
  11. Farradane, J. E. L. (1967). Concept organization for information retrieval. Information Storage and Retrieval, 3, 297–314.CrossRefGoogle Scholar
  12. Fillmore, C. J. (1968). The case for case. In E. Bach & R. T. Harms (Eds.), Universals in Linguistic Theory, 1–88. New York: Holt, Rinehart and Winston.Google Scholar
  13. Gaizauskas, R., & Wilks, Y. (1998). Information extraction beyond document retrieval. Journal of Documentation, 54, 70–105.CrossRefGoogle Scholar
  14. Gardin, J.-C. (1965). SYNTOL. New Brunswick, NJ: Graduate School of Library Service, Rutgers University.Google Scholar
  15. Glasgow, B., Mandell, A., Binney, D., Ghemri, L., & Fisher, D. (1998). MITA: An information-extraction approach to the analysis of free-form text in life insurance applications. AI Magazine, 19(1), 59–71Google Scholar
  16. Khoo, C., Chan, S., Niu, Y., & Ang, A. (1999). A method for extracting causal knowledge from textual databases. Singapore Journal of Library & Information Management, 28, 48–63.Google Scholar
  17. Khoo, C., Chan, S., & Niu, Y. (2000). Extracting causal knowledge from a medical database using graphical patterns. 38th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference, 336–343.Google Scholar
  18. Khoo, C., Kornfilt, J., Oddy, R., & Myaeng, S. H. (1998). Automatic extraction of cause-effect information from newspaper text without knowledge-based inferencing. Literary & Linguistic Computing, 13, 177–186.CrossRefGoogle Scholar
  19. Khoo, C., Myaeng, S. H., & Oddy, R. (2000). Using cause-effect relations in text to improve information retrieval precision. Information Processing & Management, 37, 119–145.CrossRefGoogle Scholar
  20. Kim, J.-T. (1996). Automatic phrasal pattern acquisition for information extraction from natural language texts. Journal of KISS (B), Software and Applications, 23, 95–105.Google Scholar
  21. Kim, J.-T., & Moldovan, D. I. (1995). Acquisition of linguistic patterns for knowledgebased information extraction. IEEE Transactions on Knowledge and Data Engineering, 7, 713–724.CrossRefGoogle Scholar
  22. Kishore, J. (1986). Colon Classification: Enumerated & Expanded Schedules Along with Theoretical Formulations. New Delhi: Ess Publications.Google Scholar
  23. Levy, F. (1967). On the relative nature of relational factors in classifications. Information Storage & Retrieval, 3, 315–329.CrossRefGoogle Scholar
  24. Liddy, E. D., & Myaeng, S. H. (1993). DR-LINK’s linguistic-conceptual approach to document detection. In The First Text REtrieval Conference (TREC-1) (NIST Special Publication 500–207), 1–20. Gaithersburg, MD: National Institute of Standards and Technology. Available: <http://trec.nist.gov/pubs.html> [2001, October 9].Google Scholar
  25. Harman, D. (Ed.) (1993-). The Text Retrieval Conference (TREC). [D. Harman (Ed.), TREC-1-TREC-4; D. Harman & E. Voorhees (Eds.), TREC-5-.] NIST Special Publication 500-207 (TREC-1), 500-215 (TREC-2), 500-225 (TREC-3), 500-236 (TREC-4), 500-238 (TREC-5), 500-240 (TREC-6), 500-242 (TREC-7), 500-246 (TREC-8), 500-249 (TREC-9). Gaithersburg, MD :National Institute of Standards and Technology. Available: <http://trec.nist.gov/pubs.html> [2001, October 9].Google Scholar
  26. Liu, G. Z. (1997). Semantic vector space model: Implementation and evaluation. Journal of the American Society for Information Science, 48, 395–417.CrossRefGoogle Scholar
  27. Lu, X. (1990). An application of case relations to document retrieval. Doctoral dissertation, University of Western Ontario.Google Scholar
  28. Marega, R., & Pazienza, M. T. (1994). CoDHIR: An information retrieval system based on semantic document representation. Journal of Information Science, 20, 399–412.CrossRefGoogle Scholar
  29. Michalski, R. (1983). A theory and methodology of inductive learning. Artificial Intelligence, 20, 111–161.CrossRefMathSciNetGoogle Scholar
  30. Mitchell, T. (1982). Generalization as search. Artificial Intelligence, 18, 203–226.CrossRefMathSciNetGoogle Scholar
  31. MUC-3. (1991). Third Message Understanding Conference (MUC-3). San Mateo, CA: Morgan Kaufmann.Google Scholar
  32. MUC-4. (1992). Fourth Message Understanding Conference (MUC-3). San Mateo, CA: Morgan Kaufmann.Google Scholar
  33. MUC-5. (1993). Fifth Message Understanding Conference (MUC-5). San Francisco: Morgan Kaufmann.Google Scholar
  34. MUC-6. (1995). Sixth Message Understanding Conference (MUC-6). San Francisco: Morgan Kaufmann.Google Scholar
  35. MUC-7. (2000). Message Understanding Conference Proceedings (MUC-7) [Online]. Available: http://www.muc.saic.com/proceedings/muc_7_toc.html.Google Scholar
  36. Myaeng, S. H., & Liddy, E. D. (1993). Information retrieval with semantic representation of texts. Proceedings of the 2nd Annual Symposium on Document Analysis and Information Retrieval, 201–215.Google Scholar
  37. Myaeng, S. H., Khoo, C., & Li, M. (1994). Linguistic processing of text for a large-scale conceptual information retrieval system. Conceptual Structures: Current Practices: Second International Conference on Conceptual Structures, ICCS ’94, 69–83.Google Scholar
  38. Nishida, F., & Takamatsu, S. (1982). Structured-information extraction from patent-claim sentences. Information Processing & Management, 18, 1–13.CrossRefGoogle Scholar
  39. Ranganathan, S. R. (1965). The Colon Classication. New Brunswick, N.J.: Graduate School of Library Service, Rutgers University.Google Scholar
  40. Rau, L. (1987). Knowledge organization and access in a conceptual information system. Information Processing & Management, 23, 269–283.CrossRefGoogle Scholar
  41. Rau, L. F., Jacobs, P. S., & Zernik, U. (1989). Information extraction and text summarization using linguistic knowledge acquisition. Information Processing & Management, 25, 419–428.CrossRefGoogle Scholar
  42. Riloff, E. (1993). Automatically constructing a dictionary for information extraction tasks. Proceedings of the Eleventh National Conference on Artificial Intelligence, 811–816.Google Scholar
  43. Riloff, E. (1996). An empirical study of automated dictionary construction for information extraction in three domains. Artifical Intelligence, 85, 101–134CrossRefGoogle Scholar
  44. Sager, N. (1981). Natural Language Information Processing: A Computer Grammar of English and its Applications. Reading, MA: Addison-Wesley.Google Scholar
  45. Sager, N., Lyman, M., Tick, L. J., Nhàn, N. T., Bucknall, C. E. (1994). Natural language processing of asthma discharge summaries for the monitoring of patient care. Proceedings ofSeventeenth Annual Symposium on Computer Applications in Medical Care, 265–268.Google Scholar
  46. Salton, G., Yang, C. S., & Yu, C. T. (1975). A theory of term importance in automatic text analysis. Journal of the American Society for Information Science, 26, 33–44.CrossRefGoogle Scholar
  47. Soderland, S., Aronow, D., Fisher, D., Aseltine, J., & Lehnert, W. (1995). Machine Learning of Text-analysis Rules for Clinical Records (Technical Report, TE-39). Amherst, MA: University of Massachusetts, Dept. of Computer Science.Google Scholar
  48. Soderland, S., Fisher, D., Aseltine, J. & Lehnert, W. (1996). Issues in inductive learning of domain-specific text extraction rules. In S. Wermter, E. Riloff, & G. Scheler (Eds.), Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing, 290–301. Berlin: Springer Verlag.CrossRefGoogle Scholar
  49. Smeaton, A. F., & van Rijsbergen, C. J. (1988). Experiments on incorporating syntactic processing of user queries into a document retrieval strategy. Proceedings of the Eleventh Annuallnternational ACM/SIGIR Conference on Research and Development in Information Retrieval, 31–51.Google Scholar
  50. Somers, H. L. (1987). Valency and Case in Computational Linguistics. Edinburgh: Edinburgh University Press.Google Scholar
  51. Sowa, J. F. (1984). Conceptual Structures: Information Processing in Mind And Machine. Reading, MA: Addison-Wesley.zbMATHGoogle Scholar
  52. Van Rijsbergen, C. J. (1979). Information Retrieval. London: Butterworths.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • Christopher Khoo
    • 1
  • Sung Hyon Myaeng
    • 2
  1. 1.Division of Information StudiesNanyang Technological UniversitySingapore
  2. 2.Department of Computer ScienceChungnam National UniversityKorea

Personalised recommendations