Comparing Machine Learning Classification Approaches for Predicting Expository Text Difficulty [Konferenzbericht] Paper presented at the International Florida Artificial Intelligence Research Society Conference (31st, Melbourne, FL, May 2018).

Autor/inn/en	Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S.
Titel	Comparing Machine Learning Classification Approaches for Predicting Expository Text Difficulty [Konferenzbericht] Paper presented at the International Florida Artificial Intelligence Research Society Conference (31st, Melbourne, FL, May 2018).
Quelle	(2018), (6 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Artificial Intelligence; Classification; Comparative Analysis; Prediction; Accuracy; Natural Language Processing; Readability; Expository Writing; Readability Formulas; Flesch Kincaid Grade Level Formula + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Classification system; Klassifikation; Klassifikationssystem; Vorhersage; Natürliche Sprache; Lesbarkeit
Abstract	While hierarchical machine learning approaches have been used to classify texts into different content areas, this approach has, to our knowledge, not been used in the automated assessment of text difficulty. This study compared the accuracy of four classification machine learning approaches (flat, one-vs-one, one-vs-all, and hierarchical) using natural language processing features in predicting human ratings of text difficulty for two sets of texts. The hierarchical classification was the most accurate for the two text sets considered individually (Set A, 77.78%; Set B, 82.05%), while the non-hierarchical approaches, one-vs-one and one-vs-all, performed similar to the hierarchical classification for the combined set (71.43%). These findings suggest both promise and limitations for applying hierarchical approaches to text difficulty classification. It may be beneficial to apply a recursive top-down approach to discriminate the subsets of classes that are at the top of the hierarchy and less related, and then further separate the classes into subsets that may be more similar to one other. These results also suggest that a single approach may not always work for all types of datasets and that it is important to evaluate which machine learning approach and algorithm works best for particular datasets. The authors encourage more work in this area to help suggest which types of algorithms work best as a function of the type of dataset. (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01