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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

2. Natural Language Understanding for Information Fusion

verfasst von : Stuart C. Shapiro, Daniel R. Schlegel

Erschienen in: Fusion Methodologies in Crisis Management

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Introduction
Nächstes Kapitel Cognitive Aspects of Higher Level Fusion
Fußnoten
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.
 
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.
 
Literatur
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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 188–207CrossRef
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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)
Metadaten
Titel
Natural Language Understanding for Information Fusion
verfasst von
Stuart C. Shapiro
Daniel R. Schlegel
Copyright-Jahr
2016
Buch
Fusion Methodologies in Crisis Management

Print ISBN: 978-3-319-22526-5

Electronic ISBN: 978-3-319-22527-2

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-22527-2_2

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