Title :
Mover: a machine learning tool to assist in the reading and writing of technical papers
Author :
Anthony, Laurence ; Lashkia, George V.
Author_Institution :
Dept. of Inf. & Comput. Eng., Okayama Univ. of Sci., Japan
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.
Keywords :
intelligent tutoring systems; learning (artificial intelligence); technical presentation; word processing; English grammar; Mover; discourse analysis; information technology; machine learning tool; research article abstracts; technical paper reading; technical paper writing; vocabulary; Abstracts; Information technology; Knowledge engineering; Machine learning; Software tools; Solids; Supervised learning; System testing; Vocabulary; Writing;
Journal_Title :
Professional Communication, IEEE Transactions on
DOI :
10.1109/TPC.2003.816789