Mover: A Machine Learning Tool to Assist in the Reading and Writing of Technical Papers

Laurence Anthony*, George V. Lashkia

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


When faced with the tasks of reading and writing a complex technical paper, many nonnative scientists and engineers who have a solid background in English grammar and vocabulary lack an adequate knowledge of commonly used structural patterns at the discourse level. In this paper, we propose a novel computer software tool that can assist these people in the understanding and construction of technical papers, by automatically identifying the structure of writing in different fields and disciplines. The system is tested using research article abstracts and is shown to be a fast, accurate, and useful aid in the reading and writing process.

Original languageEnglish
Pages (from-to)185-193
Number of pages9
JournalIEEE Transactions on Professional Communication
Issue number3
Publication statusPublished - 2003 Sept
Externally publishedYes


  • Abstract
  • Computer
  • Discourse analysts
  • Genre
  • Information technology
  • Machine learning
  • Mover
  • Research article (RA)
  • Structure
  • Supervised learning

ASJC Scopus subject areas

  • Industrial relations
  • Electrical and Electronic Engineering


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