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

Laurence Anthony, George V. Lashkia

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

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
Volume46
Issue number3
DOIs
Publication statusPublished - 2003 Sep
Externally publishedYes

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Learning systems
Engineers
Machine learning
Discourse
Grammar

Keywords

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

ASJC Scopus subject areas

  • Engineering (miscellaneous)

Cite this

Mover : A Machine Learning Tool to Assist in the Reading and Writing of Technical Papers. / Anthony, Laurence; Lashkia, George V.

In: IEEE Transactions on Professional Communication, Vol. 46, No. 3, 09.2003, p. 185-193.

Research output: Contribution to journalArticle

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