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

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.

[1]  Jianjun Yu,et al.  Towards Topic Trend Prediction on a Topic Evolution Model with Social Connection , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[2]  A. Graesser,et al.  Question Asking During Tutoring , 1994 .

[3]  Daniel Barbará,et al.  On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[4]  Nick Koudas,et al.  TwitterMonitor: trend detection over the twitter stream , 2010, SIGMOD Conference.

[5]  Brendan T. O'Connor,et al.  From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series , 2010, ICWSM.

[6]  Jon M. Kleinberg,et al.  Bursty and Hierarchical Structure in Streams , 2002, Data Mining and Knowledge Discovery.

[7]  Arthur C. Graesser,et al.  Similarity Between Semantic Spaces , 2005 .

[8]  Michael N Jones,et al.  Representing word meaning and order information in a composite holographic lexicon. , 2007, Psychological review.

[9]  Gabriella Vigliocco,et al.  Integrating experiential and distributional data to learn semantic representations. , 2009, Psychological review.

[10]  Arthur C. Graesser,et al.  AutoTutor Lite , 2009, AIED.

[11]  Takako Aikawa,et al.  Detecting Inter-domain Semantic Shift using Syntactic Similarity , 2006, LREC.

[12]  Peter W. Foltz,et al.  An introduction to latent semantic analysis , 1998 .

[13]  T. Landauer,et al.  A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. , 1997 .

[14]  Arthur C. Graesser,et al.  AutoTutor: an intelligent tutoring system with mixed-initiative dialogue , 2005, IEEE Transactions on Education.

[15]  Chung-Hong Lee,et al.  Mining spatio-temporal information on microblogging streams using a density-based online clustering method , 2012, Expert Syst. Appl..

[16]  Gilad Mishne,et al.  Leave a Reply: An Analysis of Weblog Comments , 2006 .

[17]  Vincent Aleven,et al.  An effective metacognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor , 2002, Cogn. Sci..

[18]  Thomas A. Schreiber,et al.  The University of South Florida free association, rhyme, and word fragment norms , 2004, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[19]  J. Firth,et al.  Papers in linguistics, 1934-1951 , 1957 .

[20]  Curt Burgess,et al.  From simple associations to the building blocks of language: Modeling meaning in memory with the HAL model , 1998 .