Abstract
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.
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Hu, X., Nye, B.D., Gao, C., Huang, X., Xie, J., Shubeck, K. (2014). Semantic Representation Analysis: A General Framework for Individualized, Domain-Specific and Context-Sensitive Semantic Processing. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Foundations of Augmented Cognition. Advancing Human Performance and Decision-Making through Adaptive Systems. AC 2014. Lecture Notes in Computer Science(), vol 8534. Springer, Cham. https://doi.org/10.1007/978-3-319-07527-3_4
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DOI: https://doi.org/10.1007/978-3-319-07527-3_4
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