Use of Machine Learning and Computational Linguistics to Understand Technical Instruction Authorship
| Autor: | Javier Bustos Jaimes, Taylor Powell, Rahul Sharan Renu, Lynn Hanson |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Computer science media_common.quotation_subject Usability Machine learning computer.software_genre Compliance (psychology) law.invention Instruction set law Technical communication CLARITY Quality (business) Artificial intelligence Computational linguistics business computer media_common |
| Zdroj: | Volume 3: 20th International Conference on Advanced Vehicle Technologies; 15th International Conference on Design Education. |
| DOI: | 10.1115/detc2018-85612 |
| Popis: | This article describes the use of machine learning and Natural Language Processing to determine the quality of existing technical instructions. Technical instructions were gathered from publicly available, online sources and analyzed to determine their usability and adherence level to published guidelines in the engineering and in the technical communication fields. Although some organizations have established guidelines unique to their industries, analysis of the randomly-selected instruction sets shows that documents often fall short of targets associated with usability, clarity, and benefit to the audience. The performed analyses show that over fifty percent of the instructions lack compliance with at least one established guideline.Copyright © 2018 by ASME |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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