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

Laurence Anthony; George V. Lashkia

doi:10.1109/TPC.2003.816789

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 journal › Article › peer-review

34 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 language	English
Pages (from-to)	185-193
Number of pages	9
Journal	IEEE Transactions on Professional Communication
Volume	46
Issue number	3
DOIs	https://doi.org/10.1109/TPC.2003.816789
Publication status	Published - 2003 Sept
Externally published	Yes

Keywords

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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10.1109/TPC.2003.816789

Cite this

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KW - Genre

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

Abstract

Keywords

ASJC Scopus subject areas

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