Researching the Research: Applying Machine Learning Techniques to Dissertation Classification  

作　　者：Suzanna Schmeelk Tonya L.Fields Lisa R.Ellrodt Ion C.Freeman Ashley J.Haigler 

机构地区：[1]St.John's University,United States [2]Pace University,United States

出　　处：《Journal of Computer Science Research》2020年第4期7-15,共9页计算机科学研究（英文）

摘　　要：This research examines industry-based dissertation research in a doctoralcomputing program through the lens of machine learning algorithms todetermine if natural language processing-based categorization on abstractsalone is adequate for classification. This research categorizes dissertationby both their abstracts and by their full-text using the GraphLabCreate library from Apple’s Turi to identify if abstract analysis is anadequate measure of content categorization, which we found was not. Wealso compare the dissertation categorizations using IBM’s Watson Discoverydeep machine learning tool. Our research provides perspectiveson the practicality of the manual classification of technical documents;and, it provides insights into the: (1) categories of academic work createdby experienced fulltime working professionals in a Computing doctoralprogram, (2) viability and performance of automated categorization of theabstract analysis against the fulltext dissertation analysis, and (3) natuallanguage processing versus human manual text classification abstraction.

关 键 词：Machine learning Natural language processing(NLP) Abstract vs fulltext dissertation analysis Industry-based Dissertation research classification GraphLab Create library IBM Watson Discovery 

分 类 号：H31[语言文字—英语]