Skip to main content
SpringerLink
Log in

Memory reconsolidation for natural language processing

  • Research Article
  • Published:
Cognitive Neurodynamics Aims and scope Submit manuscript

Abstract

We propose a model of memory reconsolidation that can output new sentences with additional meaning after refining information from input sentences and integrating them with related prior experience. Our model uses available technology to first disambiguate the meanings of words and extracts information from the sentences into a structure that is an extension to semantic networks. Within our long-term memory we introduce an action relationships database reminiscent of the way symbols are associated in brain, and propose an adaptive mechanism for linking these actions with the different scenarios. The model then fills in the implicit context of the input and predicts relevant activities that could occur in the context based on a statistical action relationship database. The new data both of the more complete scenario and of the statistical relationships of the activities are reconsolidated into memory. Experiments show that our model improves upon the existing reasoning tool suggested by MIT Media lab, known as ConceptNet.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price includes VAT (Netherlands)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Charniak E (1991) Bayesian networks without tears. AI Magazine 12:50–63

    Google Scholar 

  • Friedman N (1998) The Bayesian structural em algorithm. In: Mora GFCaS (ed) Uncertainty in artificial intelligence: Proceedings of the fourteenth conference. Morgan Kaufmann, Madison, Wisconsin, pp 129–138

  • Heckerman D (1999) A tutorial on learning with Bayesian networks. In: Jordan M (ed) Learning in graphical models. MIT Press, Cambridge, MA

  • Kipper K, Korhonen A, Ryant N, Palmer M (2006) Extending VerbNet with novel verb classes. In: Proceedings of the 5th international conference on language resources and evaluation. Genoa, Italy

  • Liu H, Singh P (2004a) ConceptNet: a practical commonsense reasoning toolkit. BT Technol J 22(4):211--226, Kluwer Academic Publishers

    Google Scholar 

  • Liu H, Singh P (2004b) Commonsense reasoning in and over natural language. In: Proceedings of the 8th international conference on knowledge-based intelligent information & engineering systems (KES’2004). Wellington, New Zealand, September 22–24. Lecture notes in artificial intelligence, Springer

  • Loftus EF (1975) A spreading-activation theory of semantic processing. Psychol Rev 82(6):407–428

    Article  Google Scholar 

  • Miller G (1998) Nouns in WordNet. The MIT Press, USA

    Google Scholar 

  • Miller G, Beckwith R, Fellbaum C, Gross D, Miller KJ (1990) Introduction to WordNet: an on-line lexical database. Int J Lexicography 3:235–244

    Article  Google Scholar 

  • Mitchell TM, Shinkareva SV, Carlson A, Chang K-M, Malave VL, Mason RA, Just MA (2008) Predicting human brain activity associated with the meanings of nouns. Science 320:1191–1195

    Article  CAS  PubMed  Google Scholar 

  • Mizraji E, Pomi A, Valle-Lisboa JC (2009) Dynamic searching in the brain. Cogn Neurodyn. doi:10.007/s11571-009-9084-2

  • Murphy KP (2002) Dynamic bayesian networks: representation, inference and learning. University of California, Berkeley, p 268

    Google Scholar 

  • Niculescu-mizil A (2007) Inductive transfer for Bayesian network structure learning. In: Shen MMaX (ed) Proceedings of the 11th international conference on AI and statistics (AISTATS), vol. 2, pp 339–346

  • Riccio DC, Millin PM, Bogart AR (2006) Reconsolidation: a brief history, a retrieval view, and some recent issues. Learn Mem 13:536–544

    Article  PubMed  Google Scholar 

  • Richens RH (1958) Interlingual machine translation. Comput J 1:144–147

    Article  Google Scholar 

  • Sara JS (2000) Retrieval and reconsolidation: toward a neurobiology of remembering. Learn Mem 7:73–84

    Article  CAS  PubMed  Google Scholar 

  • Sowa JF (1974) Conceptual graphs for a data base interface. IBM J Res Dev 20:336–357

    Article  Google Scholar 

  • Sowa JF (1987) Semantic networks. Encycl Artif Intell

  • Tulving E, Thomson DM (1973) Encoding specificity and retrieval processes in episodic memory. Psychol Rev 80:352–373

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the ONR grant N000140910069 and partly by Chinese Scholarship Council.

Author information

Authors and Affiliations

  1. BINDS laboratory, Department of Computer Science, University of Massachusetts Amherst, 140 Governor’s Drive, Amherst, MA, 01003, USA

    Kun Tu, David G. Cooper & Hava T. Siegelmann

  2. College of Computer Science and Engineering, South China University of Technology, 510640, Guangzhou, People‘s Republic of China

    Kun Tu

  3. Program of Evolutionary Dynamics, Harvard University, One Brattle Square, Suite 6, Cambridge, MA, 02138-3758, USA

    Hava T. Siegelmann

Authors
  1. Kun Tu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David G. Cooper
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hava T. Siegelmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hava T. Siegelmann.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tu, K., Cooper, D.G. & Siegelmann, H.T. Memory reconsolidation for natural language processing. Cogn Neurodyn 3, 365–372 (2009). https://doi.org/10.1007/s11571-009-9097-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11571-009-9097-x

Keywords

Search

Navigation