Robbie Haertel


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An Analytic and Empirical Evaluation of Return-on-Investment-Based Active Learning
Robbie Haertel | Eric Ringger | Kevin Seppi | Paul Felt
Proceedings of the 9th Linguistic Annotation Workshop

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Early Gains Matter: A Case for Preferring Generative over Discriminative Crowdsourcing Models
Paul Felt | Kevin Black | Eric Ringger | Kevin Seppi | Robbie Haertel
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Momresp: A Bayesian Model for Multi-Annotator Document Labeling
Paul Felt | Robbie Haertel | Eric Ringger | Kevin Seppi
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Data annotation in modern practice often involves multiple, imperfect human annotators. Multiple annotations can be used to infer estimates of the ground-truth labels and to estimate individual annotator error characteristics (or reliability). We introduce MomResp, a model that incorporates information from both natural data clusters as well as annotations from multiple annotators to infer ground-truth labels and annotator reliability for the document classification task. We implement this model and show dramatic improvements over majority vote in situations where both annotations are scarce and annotation quality is low as well as in situations where annotators disagree consistently. Because MomResp predictions are subject to label switching, we introduce a solution that finds nearly optimal predicted class reassignments in a variety of settings using only information available to the model at inference time. Although MomResp does not perform well in annotation-rich situations, we show evidence suggesting how this shortcoming may be overcome in future work.


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First Results in a Study Evaluating Pre-annotation and Correction Propagation for Machine-Assisted Syriac Morphological Analysis
Paul Felt | Eric Ringger | Kevin Seppi | Kristian Heal | Robbie Haertel | Deryle Lonsdale
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

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 bad pre-annotations 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.


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Parallel Active Learning: Eliminating Wait Time with Minimal Staleness
Robbie Haertel | Paul Felt | Eric K. Ringger | Kevin Seppi
Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing

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A Probabilistic Morphological Analyzer for Syriac
Peter McClanahan | George Busby | Robbie Haertel | Kristian Heal | Deryle Lonsdale | Kevin Seppi | Eric Ringger
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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CCASH: A Web Application Framework for Efficient, Distributed Language Resource Development
Paul Felt | Owen Merkling | Marc Carmen | Eric Ringger | Warren Lemmon | Kevin Seppi | Robbie Haertel
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We introduce CCASH (Cost-Conscious Annotation Supervised by Humans), an extensible web application framework for cost-efficient annotation. CCASH provides a framework in which cost-efficient annotation methods such as Active Learning can be explored via user studies and afterwards applied to large annotation projects. CCASH’s architecture is described as well as the technologies that it is built on. CCASH allows custom annotation tasks to be built from a growing set of useful annotation widgets. It also allows annotation methods (such as AL) to be implemented in any language. Being a web application framework, CCASH offers secure centralized data and annotation storage and facilitates collaboration among multiple annotations. By default it records timing information about each annotation and provides facilities for recording custom statistics. The CCASH framework has been used to evaluate a novel annotation strategy presented in a concurrently published paper, and will be used in the future to annotate a large Syriac corpus.

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Tag Dictionaries Accelerate Manual Annotation
Marc Carmen | Paul Felt | Robbie Haertel | Deryle Lonsdale | Peter McClanahan | Owen Merkling | Eric Ringger | Kevin Seppi
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Expert human input can contribute in various ways to facilitate automatic annotation of natural language text. For example, a part-of-speech tagger can be trained on labeled input provided offline by experts. In addition, expert input can be solicited by way of active learning to make the most of annotator expertise. However, hiring individuals to perform manual annotation is costly both in terms of money and time. This paper reports on a user study that was performed to determine the degree of effect that a part-of-speech dictionary has on a group of subjects performing the annotation task. The user study was conducted using a modular, web-based interface created specifically for text annotation tasks. The user study found that for both native and non-native English speakers a dictionary with greater than 60% coverage was effective at reducing annotation time and increasing annotator accuracy. On the basis of this study, we predict that using a part-of-speech tag dictionary with coverage greater than 60% can reduce the cost of annotation in terms of both time and money.

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Automatic Diacritization for Low-Resource Languages Using a Hybrid Word and Consonant CMM
Robbie Haertel | Peter McClanahan | Eric K. Ringger
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics


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Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing
Eric Ringger | Robbie Haertel | Katrin Tomanek
Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing


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Assessing the Costs of Machine-Assisted Corpus Annotation through a User Study
Eric Ringger | Marc Carmen | Robbie Haertel | Kevin Seppi | Deryle Lonsdale | Peter McClanahan | James Carroll | Noel Ellison
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Fixed, limited budgets often constrain the amount of expert annotation that can go into the construction of annotated corpora. Estimating the cost of annotation is the first step toward using annotation resources wisely. We present here a study of the cost of annotation. This study includes the participation of annotators at various skill levels and with varying backgrounds. Conducted over the web, the study consists of tests that simulate machine-assisted pre-annotation, requiring correction by the annotator rather than annotation from scratch. The study also includes tests representative of an annotation scenario involving Active Learning as it progresses from a naïve model to a knowledgeable model; in particular, annotators encounter pre-annotation of varying degrees of accuracy. The annotation interface lists tags considered likely by the annotation model in preference to other tags. We present the experimental parameters of the study and report both descriptive and inferential statistics on the results of the study. We conclude with a model for estimating the hourly cost of annotation for annotators of various skill levels. We also present models for two granularities of annotation: sentence at a time and word at a time.

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Assessing the Costs of Sampling Methods in Active Learning for Annotation
Robbie Haertel | Eric Ringger | Kevin Seppi | James Carroll | Peter McClanahan
Proceedings of ACL-08: HLT, Short Papers


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Active Learning for Part-of-Speech Tagging: Accelerating Corpus Annotation
Eric Ringger | Peter McClanahan | Robbie Haertel | George Busby | Marc Carmen | James Carroll | Kevin Seppi | Deryle Lonsdale
Proceedings of the Linguistic Annotation Workshop