From Generalization of Syntactic Parse Trees to Conceptual Graphs

  • Boris A. Galitsky
  • Gábor Dobrocsi
  • Josep Lluis de la Rosa
  • Sergey O. Kuznetsov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6208)


We define sentence generalization and generalization diagrams as a special sort of conceptual graphs which can be constructed automatically from syntactic parse trees and support semantic classification task. Similarity measure between syntactic parse trees is developed as a generalization operation on the lists of sub-trees of these trees. The diagrams are representation of mapping between the syntactic generalization level and semantic generalization level (anti-unification of logic forms). Generalization diagrams are intended to be more accurate semantic representation than conventional conceptual graphs for individual sentences because only syntactic commonalities are represented at semantic level.


Parse Tree Semantic Level Question Answering Inductive Logic Programming Conceptual Graph 
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. 1.
    Allen, J.F.: Natural Language Understanding. Benjamin Cummings (1987)Google Scholar
  2. 2.
    Banko, M., Cafarella, J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, Hyderabad, India, pp. 2670–2676. AAAI Press, Menlo Park (2007)Google Scholar
  3. 3.
    Dzikovska, M., Swift, M., Allen, J., de Beaumont, W.: Generic parsing for multi-domain semantic interpretation. In: International Workshop on Parsing Technologies (Iwpt 2005), Vancouver BC (2005)Google Scholar
  4. 4.
    Cardie, C., Mooney, R.J.: Machine Learning and Natural Language. Machine Learning 1(5) (1999)Google Scholar
  5. 5.
    Galitsky, B.: Natural Language Question Answering System: Technique of Semantic Headers. Advanced Knowledge International, Australia (2003)Google Scholar
  6. 6.
    Robinson, J.A.: A machine-oriented logic based on the resolution principle. Journal of the Association for Computing Machinery 12, 23–41 (1965)zbMATHMathSciNetGoogle Scholar
  7. 7.
    Ravichandran, D., Hovy, E.: Learning surface text patterns for a Question Answering system. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002), Philadelphia, PA (2002)Google Scholar
  8. 8.
    Durme, B.V., Huang, Y., Kupsc, A., Nyberg, E.: Towards light semantic processing for question answering. In: HLT Workshop on Text Meaning (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Boris A. Galitsky
    • 1
  • Gábor Dobrocsi
    • 2
  • Josep Lluis de la Rosa
    • 1
  • Sergey O. Kuznetsov
    • 3
  1. 1.Univ. GironaSpain
  2. 2.Univ Miskolc MiskolcHungary
  3. 3.Higher School of EconomicsRussia

Personalised recommendations