Meaning-based machine learning for information assurance
2016
Abstract This paper presents meaning-based machine learning, the use of semantically meaningful input data into machine learning systems in order to produce output that is meaningful to a human user where the semantic input comes from the Ontological Semantics Technology theory of natural language processing. How to bridge from knowledge-based natural language processing architectures to traditional machine learning systems is described to include high-level descriptions of the steps taken. These meaning-based machine learning systems are then applied to problems in information assurance and security that remain unsolved and feature large amounts of natural language text.
Keywords:
- Robot learning
- Inductive transfer
- Temporal annotation
- Algorithmic learning theory
- Active learning (machine learning)
- Natural language processing
- Universal Networking Language
- Computational learning theory
- Multi-task learning
- Machine learning
- Artificial intelligence
- Computer science
- Information extraction
- Language identification
- Instance-based learning
- Correction
- Source
- Cite
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