We develop an unsupervised semantic role labelling system that relies on the direct application of information in a predicate lexicon combined with a simple probability model. We demonstrate the usefulness of predicate lexicons for role labelling, as well as the feasibility of modifying an existing role-labelled corpus for evaluating a different set of semantic roles. We achieve a substantial improvement over an informed baseline.

Past research on digital games and virtual worlds suggests that these platforms provide multiple benefits for language learning, including positive effects on motivation and opportunities for authentic learner interaction. Despite this, adoption of these platforms in classrooms appears largely nonexistent. We provide a review of research on the use of games and virtual worlds in language learning and report on the results of a recent survey and series of interviews with university language teachers in Japan. Analysis provides insight into the challenges of adopting technology in language teaching and on the types of platforms that may ultimately see wide adoption.

No abstract

(None for technology reviews)