Semantic Representation Analysis: A General Framework for Individualized, Domain-Specific and Context-Sensitive Semantic Processing

  • Xiangen Hu
  • Benjamin D. Nye
  • Chuang Gao
  • Xudong Huang
  • Jun Xie
  • Keith Shubeck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8534)


Language agnostic methods for semantic extraction, encoding, and applications are an increasingly active research area in computational linguistics. This paper introduces an analytic framework for vector-based semantic representation called semantic representation analysis (SRA). The rationale for this framework is considered, as well as some successes and future challenges that must be addressed. A cloud-based implementation of SRA as a domain-specific semantic processing portal has been developed. Applications of SRA in three different areas are discussed: analysis of online text streams, analysis of the impression formation over time, and a virtual learning environment called V-CAEST that is enhanced by a conversation-based intelligent tutoring system. These use-cases show the flexibility of this approach across domains, applications, and languages.


Semantic analysis language agnostic domain vocabulary intelligent tutoring systems 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiangen Hu
    • 1
    • 2
  • Benjamin D. Nye
    • 1
  • Chuang Gao
    • 2
  • Xudong Huang
    • 1
  • Jun Xie
    • 1
  • Keith Shubeck
    • 1
  1. 1.The University of MemphisMemphisUSA
  2. 2.Central China Normal UniversityChina

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