Abstract

Many projects exist whose purpose is to augment raw data with annotations that increase the usefulness of the data. The number of these projects is rapidly growing and in the age of “big data” the amount of data to be annotated is likewise growing within each project. One common use of such data is in supervised machine learning, which requires labeled data to train a predictive model. Annotation is often a very expensive proposition, particularly for structured data. The purpose of this dissertation is to explore methods of reducing the cost of creating such data sets, including annotated text corpora.We focus on active learning to address the annotation problem. Active learning employs models trained using machine learning to identify instances in the data that are most informative and least costly. We introduce novel techniques for adapting vanilla active learning to situations wherein data instances are of varying benefit and cost, annotators request work “on-demand,” and there are multiple, fallible annotators of differing levels of accuracy and cost. In order to account for data instances of varying cost, we build a model of cost from real annotation data based on a user study. We also introduce a novel cost-conscious active learning algorithm which we call return-on-investment, that selects instances for annotation that contain the most benefit per unit cost. To address the issue of annotators that request instances “on-demand,” we develop a parallel, “no-wait” framework that performs computation while the annotator is annotating. As a result, annotators need not wait for the computer to determine the best instance for them to annotate—a common problem with existing approaches. Finally, we introduce a Bayesian model designed to simultaneously infer ground truth annotations from noisy annotations, infer each individual annotators accuracy, and predict its own accuracy on unseen data, without the use of a held-out set. We extend ROI-based active learning and our annotation framework to handle multiple annotators using this model. As a whole, our work shows that the techniques introduced in this dissertation reduce the cost of annotation in scenarios that are more true-to-life than previous research.

Degree

PhD

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2013-08-12

Document Type

Dissertation

Handle

http://hdl.lib.byu.edu/1877/etd6502

Keywords

active learning, cost-sensitive learning, machine learning, return-on-investment, Bayesian models, parallel active learning, natural language processing, part-of-speech tagging

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