Semantic analysis-enhanced natural language interaction in ubiquitous learning

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.