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.
This chapter is a slightly edited version of Shapiro and Rapaport (2013).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
What we call in this chapter the “syntactic KB” and the “semantic KB” were called in other papers the “syntactic propositional graph” and the “semantic propositional graph,” respectively. The reason is that, in this chapter, we are exclusively using the logic-based view of SNePS 3, whereas in those papers, we used the graph-based view of SNePS 3. Their equivalence is explained in Schlegel and Shapiro (2012).
- 3.
In a dependency parse, each token actually represents the phrase or clause headed by that token.
- 4.
Note that we are using Isa as the instance relation based on sentences like “Fido is a dog.” For the subtype (or “subclass”) relation we use Type.
- 5.
- 6.
- 7.
- 8.
The rules are shown using the actual rule syntax.
- 9.
The TokenRange, TextOf, and RootOf assertions, which are syntactic, but are retained in the semantic KB for pedigree information and to assist in the downstream scoring of entities against each other, as explained at the end of Sect. 2.7, have been omitted from the count.
References
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)
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 Science
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
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). ISIF
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 25–29
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 1000–1007
Grishman R (2011) Information extraction: capabilities and challenges. Notes prepared for the 2011 International Summer School in Language and Speech Technologies, Tarragona
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 955–962
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 79–84
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 498–505
Lehmann F (ed) (1992) Semantic networks in artificial intelligence. Pergamon Press, Oxford
National Aeronautics and Space Administration. NASA World Wind (2011). http://worldwind.arc.nasa.gov/java/
Parsons T (1990) Events in the semantics of English: a study in subatomic semantics. MIT, Cambridge
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 299–318
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 522–528
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), Th3.2.2
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 1–8
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 188–207
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 510–524
Shapiro SC, Rapaport WJ (1992) The SNePS family. Comput Math Appl 23(2–5):243–275 [Reprinted in Lehmann (1992 pp. 243–275)]
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)
University of Colorado (2012) Unified Verb Index. http://verbs.colorado.edu/verb-index/
Acknowledgements
This work has been supported by a Multidisciplinary University Research Initiative (MURI) grant (Number W911NF-09-1-0392) for “Unified Research on Network-based Hard/Soft Information Fusion,” issued by the US Army Research Office (ARO) under the program management of Dr. John Lavery.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Shapiro, S.C., Schlegel, D.R. (2016). Natural Language Understanding for Information Fusion. In: Rogova, G., Scott, P. (eds) Fusion Methodologies in Crisis Management. Springer, Cham. https://doi.org/10.1007/978-3-319-22527-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-22527-2_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22526-5
Online ISBN: 978-3-319-22527-2
eBook Packages: EngineeringEngineering (R0)