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Use of Background Knowledge in Natural Language Understanding for Information Fusion
Content Provider | CiteSeerX |
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Author | Shapiro, Stuart C. Schlegel, Daniel R. |
Abstract | 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, and stores the result, expressed in a formal knowledge representation language, in a syntactic knowledge base. This knowledge base is enhanced with ontological and geographic in-formation. Finally, Tractor applies hand-crafted syntax-semantics mapping rules to convert the enhanced syntactic knowledge base into a semantic knowledge base containing the information from the message enhanced with relevant background information. Throughout its processing, Tractor makes use of various kinds of background knowledge: knowledge of English usage; world knowledge; domain knowledge; and axiomatic knowledge. In this paper, we discuss the various kinds of background knowledge Tractor uses, and the roles they play in Tractor’s understanding |
File Format | |
Access Restriction | Open |
Subject Keyword | Background Knowledge Information Fusion Natural Language Understanding Various Kind English Usage Syntactic Knowledge Base Hand-crafted Syntax-semantics Mapping Rule Situation Assessment Enhanced Syntactic Knowledge Base Abstract Tractor Knowledge Base Relevant Background Information English Message World Knowledge Semantic Knowledge Base Text Processor Soft Information Fusion Formal Knowledge Representation Language Geographic In-formation Background Knowledge Tractor Domain Knowledge Axiomatic Knowledge |
Content Type | Text |