Semantic Analysis-Enhanced Natural Language Interaction in Ubiquitous Learning

  • Dunwei Wen
  • Yan Gao
  • Guangbing Yang
Part of the Lecture Notes in Educational Technology book series (LNET)


Natural language interaction (NLI) is vital and ubiquitous by nature in education environments. It will keep playing key roles in ubiquitous learning and even show stronger presence there. NLI may happen ubiquitously, with many varied forms of texts, bigger textual data, and different learning situations on all kinds of devices, to meet new user needs, thus pose challenges on its design and development. This chapter introduces how natural language processing (NLP) technologies can be employed to help build and improve NLI that can support ubiquitous learning. We emphasize semantic analysis such as semantic role labeling and semantic similarity, and develop and use them to enhance question and answer processing, automated question answering, and automatic text summarization that are involved in our educational systems. Our proposed approaches can improve the technology of natural language processing and help develop different NLI systems in the ubiquitous learning environments and eventually benefit learners.


Natural language processing Question answering Semantic analysis Automatic text summarization Topic modeling Ubiquitous learning 



The authors acknowledge the support of Research Incentive Grant (RIG) of Athabasca University. This work was also partly supported by NSERC, iCORE, Xerox, and the research-related gift funding by Mr. A. Markin.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.School of Computing and Information SystemsAthabasca UniversityEdmontonCanada
  2. 2.School of Information Science and EngineeringCentral South UniversityChangsha HunanP.R. China
  3. 3.School of ComputingUniversity of Eastern FinlandJoensuuFinland

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