Natural language understanding for information fusion

Stuart C. Shapiro; Daniel R. Schlegel

doi:10.1007/978-3-319-22527-2_2

Natural language understanding for information fusion

University at Buffalo, SUNY Oswego

SUNY Buffalo

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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.

Original language	English
Title of host publication	Fusion Methodologies in Crisis Management
Subtitle of host publication	Higher Level Fusion and Decision Making
Publisher	Springer International Publishing
Pages	27-45
Number of pages	19
ISBN (Electronic)	9783319225272
ISBN (Print)	9783319225265
DOIs	https://doi.org/10.1007/978-3-319-22527-2_2
State	Published - Jan 1 2016

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10.1007/978-3-319-22527-2_2

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Natural language understanding for information fusion

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