First Results in a Study Evaluating Pre-annotation and Correction Propagation for Machine-Assisted Syriac Morphological Analysis
Year: 2012
Type: conference-paper
Abstract: Manual annotation of large textual corpora can be cost-prohibitive, especially for rare and under-resourced languages.One potential solution is pre-annotation: asking human annotators to correct sentences that have already been annotated, usually by a machine.Another potential solution is correction propagation: using annotator corrections to dynamically improve to the remaining pre-annotations within the current sentence.The research presented in this paper employs a controlled user study to discover under what conditions these two machine-assisted annotation techniques are effective in increasing annotator speed and accuracy and thereby reducing the cost for the task of morphologically annotating texts written in classical Syriac.A preliminary analysis of the data indicates that pre-annotations improve annotator accuracy when they are at least 60% accurate, and annotator speed when they are at least 80% accurate.This research constitutes the first systematic evaluation of pre-annotation and correction propagation together in a controlled user study.
Institution: Brigham Young University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2251975282