Semantic representation analysis: A general framework for individualized, domain-specific and context-sensitive semantic processing
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. © 2014 Springer International Publishing Switzerland.
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Recommended Citation
Hu, X., Nye, B., Gao, C., Huang, X., Xie, J., & Shubeck, K. (2014). Semantic representation analysis: A general framework for individualized, domain-specific and context-sensitive semantic processing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8534 LNAI, 35-46. https://doi.org/10.1007/978-3-319-07527-3_4
