Annotated textual corpora are an essential language resource, facilitating manual search and discovery as well as supporting supervised Natural Language Processing (NLP) techniques designed to accomplishing a variety of useful tasks. However, manual annotation of large textual corpora can be cost-prohibitive, especially for rare and under-resourced languages. For this reason, developers of annotated corpora often attempt to reduce annotation cost by offering annotators various forms of machine assistance intended to increase annotator speed and accuracy. This thesis contributes to the field of annotated corpus development by providing tools and methodologies for empirically evaluating the effectiveness of machine assistance techniques. This allows developers of annotated corpora to improve annotator efficiency by choosing to employ only machine assistance techniques that make a measurable, positive difference. We validate our tools and methodologies using a concrete example. First we present CCASH, a platform for machine-assisted online linguistic annotation capable of recording detailed annotator performance statistics. We employ CCASH to collect data detailing the performance of annotators engaged in syriac morphological analysis in the presence of two machine assistance techniques: pre-annotation and correction propagation. We conduct a preliminary analysis of the data using the traditional approach of comparing mean data values. We then demonstrate a Bayesian analysis of the data that yields deeper insights into our data. Pre-annotation is shown to increase annotator accuracy when pre-annotations are at least 60% accurate, and annotator speed when pre-annotations are at least 80% accurate. Correction propagation's effect on accuracy is minor. The Bayesian analysis indicates that correction propagation has a positive effect on annotator speed after accounting for the effects of the particular visual mechanism we employed to implement it.



College and Department

Physical and Mathematical Sciences; Computer Science



Date Submitted


Document Type





Syriac, Bayesian methods, Annotated Corpora, Machine-Assisted Annotation, Machine Assistance