Developing a visual temporal modeller: Applying an extensible nlp system to support learners' understanding of tense and aspect in english

  • Authors:

  • John Kerins;Allan Ramsay

  • Affiliations:

  • Department of computer science and information systems, university of chester, parkgate road, chester ch1 4bj, uk (e-mail: j.kerins@chester.ac.uk);School of computer science, university of manchester, manchester m13 9pl, uk (e-mail: allan.ramsay@manchester.ac.uk)

  • Venue:
  • ReCALL
  • Year:
  • 2012

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Abstract

This paper reports on the development of a prototype tool which shows how learners can be helped to reflect upon the accuracy of their writing. Analysis of samples of freely written texts by intermediate and advanced learners of English as a foreign language (EFL) showed evidence of weakness in the use of tense and aspect. Computational discourse modelling techniques were applied to the data to generate semantic models of fragments of the narratives with particular focus on their temporal structure. These models have been converted into dynamic graphical representations of the temporal relationships between discourse events as the narratives are written. The system also provides access to the ontology devised to model individual events and this offers learners insights into the events' semantic properties. These techniques provide the basis for a stimulating learning tool capable of capturing key elements of written narratives, and prompting learners' awareness of language use, particularly tense and aspect.