Luiz Amaral, Detmar Meurers and Ramon Ziai
Computer Assisted Language Learning 24 (1), 2011, 1-16..
Intelligent Language Tutoring Systems (ILTS) typically focus on analyzing learner input to diagnose learner errors and provide individualized feedback. Despite a long history of ILTS research (cf. Heift & Schulze, 2007), such systems are virtually absent from real-life foreign language teaching (FLT), arguably because current FLT activity design and pedagogical principles and their impact on the system architecture have generally not been considered in ILTS research (cf. Amaral & Meurers, submitted).
In this paper, we argue that a demand-driven, annotation-based Natural Language Processing (NLP) architecture is best suited to handle the demands posed by the heterogeneous learner input which results when supporting a wider range of current 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 for the teaching of Portuguese.
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Bibtex entry:
@Article{Amaral.Meurers.Ziai-11,
author = {Luiz Amaral and Detmar Meurers and Ramon Ziai},
title = {Analyzing Learner Language: Towards A Flexible
NLP Architecture for Intelligent Language Tutors},
journal = {Computer Assisted Language Learning},
volume = {24},
number = {1},
year = {2011},
pages = {1--16},
doi = {http://dx.doi.org/10.1080/09588221.2010.520674},
url = {http://purl.org/dm/papers/amaral-meurers-ziai-10.html}
}