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Why NLP Should Move Into IAS. Victor Raskin, Sergei Nirenburg, Mikhail J. Atallah, Christian F. Hempelmann, and Katrina E. Triezenberg. The Paper Plan. Applications of NLP to IAS Ontological semantics at IAS service NLP/IAS applications so far Milestones and challenges in NLP/IAS.

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why nlp should move into ias
Why NLP Should Move Into IAS

Victor Raskin, Sergei Nirenburg,

Mikhail J. Atallah,

Christian F. Hempelmann, andKatrina E. Triezenberg

the paper plan
The Paper Plan
  • Applications of NLP to IAS
  • Ontological semantics at IAS service
  • NLP/IAS applications so far
  • Milestones and challenges in NLP/IAS
applications of nlp to ias
Applications of NLP to IAS
  • IAS: Need to protect computer systems and information in them from attacks
  • IS: Protection from intrusion and unauthorized use
  • IA: Ensuring authenticity of stored and transmitted information
  • Much of the information is NL text: Enter NLP!
ontological semantics at ias service
Ontological-Semantics at IAS Service
  • Based on computer understanding of the information
  • Takes full advantage of the new technologies in computational semantics
  • Problem-driven
  • Uses 3 major resources:

1. lexicon (words of an natural language explained in terms of an ontological concept)

2. ontology (a tangled hierarchy of concepts)

3. text-meaning representation (TMR): (composes sentential meaning out of out of ontological concepts)

nlp ias applications so far
NLP/IAS Applications So Far
  • using machine translation for an additional layer of encryption;
  • generating mnemonics for random-generated passwords;
  • declassification or downgrading of classified information;
  • NL watermarking;
  • digital rights protection;
nlp ias applications so far cont d
NLP/IAS Applications So Far(cont’d)
  • forensic IAS, specifically, tracing leaks in divulging protected information;
  • tamperproofing textual data;
  • enhancing the acceptance of IAS products by the users with the help of computational humor;
  • NL chaffing
milestones and challenges in nlp ias
Milestones and challenges in NLP/IAS
  • Reaching IAS accuracy needs with semantic-representational methods
  • Extending the ontological-semantic approach to non-NL data
  • Including NL data sources as an integral part of the overall data sources in IAS applications
  • Standardizing IAS terminology on ontological-semantic basis
  • Modeling the IAS know-how ontologically for the support of routine and time-efficient measures to prevent and counteract computer attacks