Developing ICALL tools using GATE

Wood, P C

doi:10.1080/09588220802343736

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…The source code of the UIMA components will also be made available under an open-source license to encourage collaborative development with other researchers. This aspect of using UIMA as a standard architecture is in line with the recent argument of Wood (2008) for such standardization to facilitate compatibility and reusability of resources in ICALL development.…”

Section: Resultssupporting

confidence: 63%

Analyzing learner language: towards a flexible natural language processing architecture for intelligent language tutors

Amaral

Meurers

Ziai

2011

Computer Assisted Language Learning

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Intelligent language tutoring systems (ILTS) typically analyze learner input to diagnose learner language properties and provide individualized feedback. Despite a long history of ILTS research, such systems are virtually absent from real-life foreign language teaching (FLT). Taking a step toward more closely linking ILTS research to real-life FLT, in this article we investigate the connection between FLT activity design and the system architecture of an ILT system. We argue that a demand-driven, annotation-based natural language processing (NLP) architecture is well-suited to handle the demands posed by the heterogeneous learner input which results when supporting a wider range of FLT activity types. We illustrate how the unstructured information management architecture (UIMA) can be used in an ILTS, thereby connecting the specific needs of activities in foreign language teaching to the current research and development of NLP architectures in general. Making the conceptual issues concrete, we discuss the design and realization of a UIMA-based reimplementation of the NLP in the TAGARELA system, an intelligent web-based tutoring system supporting the teaching and learning of Portuguese.Keywords: intelligent language tutoring systems (ILTS); intelligent computerassisted language learning (ICALL); natural language processing (NLP); unstructured information management architecture (UIMA); demand-driven annotation-based architecture; individualized feedback

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Section: Resultssupporting

confidence: 63%