Relation between Agreement Measures on Human Labeling and Machine Learning Performance: Results from an Art History Domain

We discuss factors that affect human agreement on a semantic labeling task in the art history domain, based on the results of four experiments where we varied the number of labels annotators could assign, the number of annotators, the type and amount of training they received, and the size of the text span being labeled. Using the labelings from one experiment involving seven annotators, we investigate the relation between interannotator agreement and machine learning performance. We construct binary classifiers and vary the training and test data by swapping the labelings from the seven annotators. First, we find performance is often quite good despite lower than recommended interannotator agreement. Second, we find that on average, learning performance for a given functional semantic category correlates with the overall agreement among the seven annotators for that category. Third, we find that learning performance on the data from a given annotator does not correlate with the quali...

• Type: Conference
• Year: 2008
• Where: LREC
• Authors: Rebecca J. Passonneau, Thomas Lippincott, Tae Yano, Judith L. Klavans
• Keywords: Education, Interannotator Agreement, LREC 2008, Machine Learning, Machine Learning Performance