Natural Language Understanding for Information Fusion

Abstract

Tractor is a system for understanding English messages within the context of hard and soft information fusion for situation assessment. Tractor processes a message through text processors using standard natural language processing techniques, and represents the result in a formal knowledge representation language. The result is a hybrid syntactic-semantic knowledge base that is mostly syntactic. Tractor then adds relevant ontological and geographic information. Finally, it applies hand-crafted syntax-semantics mapping rules to convert the syntactic information into semantic information, although the final result is still a hybrid syntactic-semantic knowledge base. This chapter presents the various stages of Tractor’s natural language understanding process, with particular emphasis on discussions of the representation used and of the syntax-semantics mapping rules.

References

  1. Cunningham H, Maynard D, Bontcheva K, Tablan V (2002) GATE: a Framework and graphical development environment for robust NLP tools and applications. In: Proceedings of the 40th anniversary meeting of the Association for Computational Linguistics (ACL’02)Google Scholar
  2. Cunningham H, Maynard D, Bontcheva K, Tablan V, Aswani N, Roberts I, Gorrell G, Funk A, Roberts A, Damljanovic D, Heitz T, Greenwood MA, Saggion H, Petrak J, Li Y, Peters W (2011) Text processing with GATE (Version 6). The University of Sheffield, Department of Computer ScienceGoogle Scholar
  3. de Marneffe M-C, Manning CD (2011) Stanford typed dependencies manual. Stanford University, September 2008. Revised for Stanford Parser v. 1.6.9 in September 2011. http://nlp.stanford.edu/software/dependencies_manual.pdf
  4. Gómez-Romero J, Garcia J, Kandefer M, Llinas J, Molina JM, Patricio MA, Prentice M, Shapiro SC (2010) Strategies and techniques for use and exploitation of contextual information in high-level fusion architectures. In: Proceedings of the 13th international conference on information fusion (Fusion 2010). ISIFGoogle Scholar
  5. Graham JL (2011) A new synthetic dataset for evaluating soft and hard fusion algorithms. In: Proceedings of the SPIE defense, security, and sensing symposium: defense transformation and net-centric systems 2011, pp 25–29Google Scholar
  6. Graham JL, Rimland J, Hall DL (2011) A COIN-inspired synthetic data set for qualitative evaluation of hard and soft fusion systems. In: Proceedings of the 14th international conference on information fusion (Fusion 2011). ISIF, pp 1000–1007Google Scholar
  7. Grishman R (2011) Information extraction: capabilities and challenges. Notes prepared for the 2011 International Summer School in Language and Speech Technologies, TarragonaGoogle Scholar
  8. Gross GA, Nagi R, Sambhoos K, Schlegel DR, Shapiro SC, Tauer G (2012) Towards hard+soft data fusion: Processing architecture and implementation for the joint fusion and analysis of hard and soft intelligence data. In: Proceedings of the 15th international conference on information fusion (Fusion 2012). ISIF, pp 955–962Google Scholar
  9. Kandefer M, Shapiro SC (2009) An F-measure for context-based information retrieval. In: Lakemeyer G, Morgenstern L, Williams M-A (eds) Commonsense 2009: Proceedings of the ninth international symposium on logical formalizations of commonsense reasoning. The Fields Institute, Toronto, pp 79–84Google Scholar
  10. Kandefer M, Shapiro SC (2011) Evaluating spreading activation for soft information fusion. In: Proceedings of the 14th international conference on information fusion (Fusion 2011). ISIF, pp 498–505Google Scholar
  11. Lehmann F (ed) (1992) Semantic networks in artificial intelligence. Pergamon Press, OxfordGoogle Scholar
  12. National Aeronautics and Space Administration. NASA World Wind (2011). http://worldwind.arc.nasa.gov/java/
  13. Parsons T (1990) Events in the semantics of English: a study in subatomic semantics. MIT, CambridgeGoogle Scholar
  14. Poore AB, Lu S, Suchomel BJ (2009) Data association using multiple frame assignments. In: Liggins M, Hall D, Llinas J (eds) Handbook of multisensor data fusion, Chap. 13, 2nd edn. CRC, Boca Raton, pp 299–318Google Scholar
  15. Prentice M, Shapiro SC (2011) Using propositional graphs for soft information fusion. In: Proceedings of the 14th international conference on information fusion (Fusion 2011). ISIF, pp 522–528Google Scholar
  16. Prentice M, Kandefer M, Shapiro SC (2010) Tractor: a framework for soft information fusion. In: Proceedings of the 13th international conference on information fusion (Fusion2010), Th3.2.2Google Scholar
  17. Sambhoos K, Llinas J, Little E (2008) Graphical methods for real-time fusion and estimation with soft message data. In: Proceedings of the 11th international conference on information fusion (Fusion 2008). ISIF, pp 1–8Google Scholar
  18. Schlegel DR, Shapiro SC (2012) Visually interacting with a knowledge base using frames, logic, and propositional graphs. In: Croitoru M, Rudolph S, Wilson N, Howse J, Corby O (eds) Graph structures for knowledge representation and reasoning. Lecture notes in artificial intelligence, vol 7205. Springer, Berlin, pp 188–207CrossRefGoogle Scholar
  19. Shapiro SC (2000) An introduction to SNePS 3. In: Ganter B, Mineau GW (eds) Conceptual structures: logical, linguistic, and computational issues. Lecture notes in artificial intelligence, vol 1867. Springer, Berlin, pp 510–524CrossRefGoogle Scholar
  20. Shapiro SC, Rapaport WJ (1992) The SNePS family. Comput Math Appl 23(2–5):243–275 [Reprinted in Lehmann (1992 pp. 243–275)]Google Scholar
  21. Shapiro SC, Schlegel DR (2013) Natural language understanding for soft information fusion. In: Proceedings of the 16th international conference on information fusion (Fusion 2013) ISIF, 9 pp. (unpaginated)Google Scholar
  22. University of Colorado (2012) Unified Verb Index. http://verbs.colorado.edu/verb-index/

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.State University of New York at BuffaloBuffaloUSA

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