Extracting conceptual knowledge from text using explicit relation markers

  • Paul R. Bowden
  • Peter Halstead
  • Tony G. Rose
Eliciting Knowledge from Textual and Other Sources
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1076)


This paper describes a method for extracting knowledge from large corpora using conceptual relations such as definition and exemplification. The two major steps in this process are the identification of specific relations using positive and negative triggering, and the extraction of the conceptual information by combinatorial pattern-matching. Validation of extracted candidate text is performed by analysis of part-of-speech tag patterns. The algorithms are embodied in a robust program which is capable of attempting extraction even in the absence of part-of-speech tags in the input text. Unlike many knowledge extraction systems, the KEP program is designed to be non domain specific. Intended applications described include knowledge acquisition for automatic examination question setting and marking, and knowledge acquisition for the creation and updating of semantic nets used in a hypermedia-based tutoring system.


Noun Phrase Natural Language Processing Conceptual Relation Computational Linguistics Expository Text 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ahmad, K. and Fulford, H. Semantic Relations and their Use in Elaborating Terminology Computing Sciences Report CS-92-07, Univ. Surrey, England (1992).Google Scholar
  2. Allott, N., Fazackerley, P. and Halstead, P. Automated Assessment: Evaluating a Knowledge Architecture for Natural Language Processing Procs EXPERT SYSTEMS '94 (Cambridge, England (1994)Google Scholar
  3. Alshawi, H. Processing Dictionary Definitions with Phrasal Pattern Hierarchies Computational Linguistics 13 3–4 (1987)Google Scholar
  4. BSI (British Standards Institution) British Standard Guide to Establishment and Development of Monolingual Thesauri BS 5723:1987, ISO 2788-1986 (1987)Google Scholar
  5. Bowden, P., Halstead, P. and Rose, T. Dictionaryless English Plural Noun Singularisation Using A Corpus-Based List of Irregular Forms (paper to be presented at ICAME '96, Univ. Stockholm, Sweden) (1996)Google Scholar
  6. Carter, D. Interpreting Anaphors in Natural Language Texts Ellis Horwood (1987)Google Scholar
  7. Cruse, D. A. Lexical Semantics Cambridge University Press (1986)Google Scholar
  8. Edwards, M. A., Powell. H., and Palmer-Brown, D. A Hypermedia-based Tutoring and Knowledge Engineering System In Procs. ED-MEDIA '95 Graz, Austria (1995)Google Scholar
  9. Gang Zhu and Shadbolt, N. Mining Knowledge: The Partial Parsing of Texts Departmental paper, AI Research Group, Dept. Psychology, Univ. Nottingham (UK).Google Scholar
  10. Halliday, M. A. K. and Ruqaiya Kasan Cohesion in English Longman (1976)Google Scholar
  11. Hayes, P. J. and Mouradian, G. V. Flexible Parsing American Journal of Computational Linguistics 7 4 (1981)Google Scholar
  12. Hearst, M. A. Automatic Acquisition of Hyponyms from Large Text Corpora Procs. COLING-92, Nantes, France (1992)Google Scholar
  13. Hobbs, J. R. et al. Robust Parsing of Real-World Natural-Language Texts. In Text-Based Intelligent Systems Lawrence Erlbaum Associates (1992)Google Scholar
  14. Jacobs, P. S. Text Power and Intelligent Systems. In Text-Based Intelligent Systems Lawrence Erlbaum Associates (1992)Google Scholar
  15. Long, G., Powell, H., and Palmer-Brown, D. A Syntax-free NLP Interface for an Intelligent Tutoring Environment In Procs. CSNLP 95 (4th Int. Conf. on the Cognitive Science of Natural Language Processing, Dublin City Univ., Ireland) (1995)Google Scholar
  16. Lyons, J. Semantics Cambridge University Press (1977)Google Scholar
  17. Lytinen, S. L. and Gershman, A. ATRANS: Automatic Processing of Money Transfer Messages In Procs. 5th Nat. Conf. on Artificial Intelligence, Philadelphia (1986)Google Scholar
  18. Mittal., V. O. and Paris, C. L. Categorizing Example Types in Instructional Texts: the need to consider context Procs. AI-ED 93 (Edinburgh, 23–27 Aug. 1993)Google Scholar
  19. Morris, J. and Hirst, G. Lexical Cohesion Computed by Thesaural Relations as an Indicator of the Structure of Text Computational Linguistics 17 1 (1991)Google Scholar
  20. Rau, L. F. and Jacobs, P. S. Integrating Top-down and Bottom-up Strategies in a Text Processing System In Procs. 2nd Conf. on Applied Natural Language Processing, Morristown, NJ, USA (1988).Google Scholar
  21. Schank, R. and Abelson, R. Scripts Plans Goals and Understanding Lawrence Erlbaum Associates (1977)Google Scholar
  22. Stede, M. The Search for Robustness in Natural Language Understanding Artificial Intelligence Review 6 (1992)Google Scholar
  23. Vander Linden, K. and Martin, H. Expressing Rhetorical Relations in Instructional Text: A Case Study of The Purpose Relation Computational Linguistics 21 1 (1995)Google Scholar
  24. Young, S. R. and Hayes, P. J. Automatic Classification and Summarization of Banking Telexes In Procs. 2nd Conf. on AI Applications, IEEE Comp. Soc. (1985)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Paul R. Bowden
    • 1
  • Peter Halstead
    • 1
  • Tony G. Rose
    • 1
  1. 1.Department of ComputingNottingham Trent UniversityNottinghamEngland

Personalised recommendations