A Framework for Integrating Relational and Associational Knowledge for Comprehension

  • Lawrence A. Bookman
Part of the The Springer International Series In Engineering and Computer Science book series (SECS, volume 292)


Two important aspects of understanding a text are the ability to skim it, extracting important elements (a coarse-grain view of comprehension), and the ability to read it “deeply” (a fine-grain view of comprehension). A computational analogue that mimics skimming should include a representation of a set of semantic relationships about the text that can be used to summarize it and extract what is important. A computational analogue that supports a deep reading of the text should be able to represent the background details (nonsystematic relationships) associated with the concepts in the text, including the larger frame in which the text concepts are situated.


Semantic Memory Semantic Feature Interpretation Graph Economic Outlook Sentence Pair 
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]
    Alterman, R. (1985). A dictionary based on concept coherence. Artificial Intelligence, 25:153–186.CrossRefGoogle Scholar
  2. [2]
    Alterman, R. and Bookman, L.A. (1992). Reasoning abut the semantic memory encoding of the connectivity of events. Cognitive Science, 16(2):205–232.CrossRefGoogle Scholar
  3. [3]
    Anderson, R.C. and Pichert, J.W. (1978). Recall of previously unrecallable information following a shift in perspective. Journal of Verbal Learning and Verbal Behavior, 17:1–12.CrossRefGoogle Scholar
  4. [4]
    Berrey, L.V. (Ed.) (1962). Roget’s International Thesaurus (3rd edition). NY: Thomas Crowell Company.Google Scholar
  5. [5]
    Bookman, L.A. (1989). A connectionist scheme for modelling context. In Proceedings of the 1988 Connectionist Models Summer School, pp. 281–290. San Mateo, CA: Morgan Kaufmann.Google Scholar
  6. [6]
    Bookman, L.A. (1993). A scalable architecture for integrating associative and semantic memory. Connection Science, 5(3–4):243–273.CrossRefGoogle Scholar
  7. [7]
    Bookman, L.A. (1994). Trajectories through Knowledge Space: A Dynamic Framework for Maching Comprehension. Norwell, MA: Kluwer.Google Scholar
  8. [8]
    Charniak, E. (1986). A neat theory of marker passing. In Proceedings of the Fifth National Conference on Artificial Intelligence, pp. 584–588, San Mateo, CA: Morgan Kaufmann.Google Scholar
  9. [9]
    Chun, H. (1986). AINET-2 user’s manual. Technical Report CS-86-126, Computer Science Department, Brandeis University, Waltham, MA.Google Scholar
  10. [10]
    Chun, H.W., Bookman, L.A. and Afshartous, N. (1987). Network regions: Alternatives to the winner-take-all structure. In Proceedings of the Tenth International Joint Conference on Artificial Intelligence, pp. 380–387.Google Scholar
  11. [11]
    Fano, R.M. (1961). Transmission of Information. Cambridge, MA: MIT Press.Google Scholar
  12. [12]
    Fahlman, S. (1979). NETL: A System for Representing and Using Real-World Knowledge. Cambridge, MA: MIT Press.MATHGoogle Scholar
  13. [13]
    Feldman, J.A. and Ballard, D.H. (1982). Connectionist models and their properties. Cognitive Science, 6:205–254.CrossRefGoogle Scholar
  14. [14]
    Fillmore, C.J. (1982). Frame semantics. In The Linguistic Society of Korea (Ed.), Linguistics in the Morning Calm. Seoul: Hanshin Publishing Company.Google Scholar
  15. [15]
    Gallant, S.I. (1991). A practical approach for representing context and for performing word sense disambiguation using neural networks. Neural Computation, 3(3):293–309.CrossRefGoogle Scholar
  16. [16]
    Granger, R.H., Eiselt, K.P. and Holbrook, J.K. (1986). Parsing with parallelism: A spreading activation model of inferencing processing during text understanding. In J. Kolodner and C. Riesbeck (Eds.),Experience, Memory, and Reasoning. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  17. [17]
    Halgren, E. (1990). Insights from evoked potentials into the neuropsychological mechanisms of reading. In A.B. Scheibel and A.F. Wechsler (Eds.), Neurobiology of Higher Cognitive Function. NY: Guilford.Google Scholar
  18. [18]
    Heit, G., Smith, M.E. and Halgren, E. (1988). Neural encoding of individual words and faces in the human hippocampus and amygdala. Nature, 333:773–775.CrossRefGoogle Scholar
  19. [19]
    Hendler, J. (1986). Integrating Marker-Passing and Problem-Solving: A Spreading Activation Approach to Improved Choice in Planning. PhD thesis, Brown University, Providence, RI.Google Scholar
  20. [20]
    Hinton, G.E. (1981). Implementing semantic networks in parallel hardware. In G.E. Hinton and J.A. Anderson (Eds.), Parallel Models of Associative Memory. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  21. [21]
    Hinton, G.E., McClelland, J.L. and Rumelhart, D.E. (1986). Distributed representations. In J.L. McClelland and D.E. Rumelhart (Eds.), Parallel Distributed Processing: Explorations in the Microstructures of Cognition (Vol. 1). Cambridge, MA: MIT Press.Google Scholar
  22. [22]
    Kozminsky, E. (1977). Altering comprehension: The effect of biasing titles on comprehension. Memory and Cognition, 5:482–490.Google Scholar
  23. [23]
    Lange, T.E. and Dyer, M.G. (1989). High-level inferencing in a connectionist network. Connection Science, 1(2):181–217.CrossRefGoogle Scholar
  24. [24]
    Miikkulainen, R. (1993). Subsymbolic Natural Language Processing: An Integrated Model of Scripts, Lexicon, and Memory. Cambridge, MA: MIT Press.Google Scholar
  25. [25]
    Miller, E.K., Li, L. and Desimone, R. (1991). A neural mechanism for working and recognition memory in inferior temporal cortex. Science, 254:1377–1370.CrossRefGoogle Scholar
  26. [26]
    Miller, G.A, Beckwith, R. Fellbaum, C. Gross, D. and Miller, K. (1990). Five papers on Word Net. CSL Report 43. Cognitive Science Laboratory, Princeton University.Google Scholar
  27. [27]
    Morris, J. and Hirst, G. (1991). Lexical cohesion computed by thesaural relations as an indicator of the structure of the text. Computational Linguistics, 17(1):21–48.Google Scholar
  28. [28]
    Norvig, P. (1989). Marker passing as a weak method for text inferencing. Cognitive Science, 13(4):569–620.CrossRefGoogle Scholar
  29. [29]
    Quillian, M.R. (1968). Semantic memory. In M. Minsky (Ed.), Semantic Information Processing. Cambridge, MA: MIT Press.Google Scholar
  30. [30]
    Rolls, E.T., Baylis, G.C., Hasselmo, M.E. and Nalwa, V. (1989). The effect of learning on the face selective responses of neurons in the superior temporal sulcus of the monkey. Experimental Brain Research, 76(1): 153–164.CrossRefGoogle Scholar
  31. [31]
    Rumelhart, D.E. (1981). Understanding understanding. In H.W. Dechert and M. Raupach (Eds.), Psycholinguistic Models of Production, Norwood, NJ: Ablex Publishing.Google Scholar
  32. [32]
    Schank, R. (1982). Dynamic Memory. NY: Cambridge University Press.Google Scholar
  33. [33]
    Shastri, L. (1988). A connectionist approach to knowledge representation and limited inference. Cognitive Science, 12(3):331–392.CrossRefGoogle Scholar
  34. [34]
    Shastri, L. and Ajjanagadde, V. (1993). From simple associations to systematic reasoning: A connectionist encoding of rules, variables and dynamic bindings using temporal synchrony. Behavioral and Brain Sciences, 16(3):417–494.CrossRefGoogle Scholar
  35. [35]
    Simmons, R.F. (1973). Semantic networks: Their computation and use for understanding english sentences. In R.C. Schank and K.M. Colby (Eds.), Computer Models of Thought and Language. San Francisco, CA: W.H. Freeman and Company.Google Scholar
  36. [36]
    Sowa, J.F. (1984). Conceptual Structures: Information Processing in Minds and Machines. Reading, MA: Addison-Wesley.Google Scholar
  37. [37]
    St. John, M.F. (1992). The story gestalt: A model of knowledge-intensive processes in text comprehension. Cognitive Science, 16:271–306.CrossRefGoogle Scholar
  38. [38]
    Sun, R. (1991). Connectionist models of rule-based reasoning. In Proceedings of the Thirteenth Annual Cognitive Science Conference, pp. 437–442.Google Scholar
  39. [39]
    Sun, R. (1994). Integrating Rules and Connectionism for Robust Commonsense Reasoning. New York: John Wiley.MATHGoogle Scholar
  40. [40]
    Thorndike, P.W. and Yekovich, F.R. (1980). A critique of schema-based theories of human story memory. Poetics, 9:23–49.CrossRefGoogle Scholar
  41. [41]
    Velardi, P., Pazienza, M.T., and Fasolo, M. (1991). How to encode semantic knowledge: A method for meaning representation and computer-aided acquisition. Computational Linguistics, 17:153–170.Google Scholar
  42. [42]
    Waltz, D.L. and Pollack, J.B. (1985). Massively parallel parsing: A strongly interactive model of natural language interpretation. Cognitive Science, 9:52–74.Google Scholar
  43. [43]
    Waltz, D.L. (1982). Event shape diagrams. In Proceedings of Second National Conference on Artificial Intelligence, pp. 84–87. Google Scholar

Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Lawrence A. Bookman
    • 1
  1. 1.Sun Microsystems LaboratoriesChelmsford

Personalised recommendations