Producing the required amounts of training data for machine learning and NLP tasks often involves human annotators doing very repetitive and monotonous work. In this paper, we present and evaluate our novel annotation framework DALPHI, which facilitates the annotation process by providing the annotator with suggestions generated by an automated, active-learning based assistance system. In a study with 66 participants, we demonstrate on the exemplary task of annotating named entities in text documents that with this assistance system the annotation processes can be improved with respect to the quality and quantity of produced annotations, even if the pre-annotations provided by the assistance system are at a recall level of only 50 .
provide a comprehensive list of web-based, collaborative annotation tools, most of which focus on text annotation (highlighting spans and relations between spans). Two frequently used tools are @cite_4 and @cite_3 @cite_9 , which already support the annotator by providing pre-annotations. However, these tools only provide limited support for custom ML models to generate pre-annotations with or for active learning setups, i.e., to automatically prioritize those documents for annotation that would be most informative for the ML model. Additionally, their UIs can not be easily customized to fit novel annotation tasks.
@cite_9 @cite_4 @cite_3
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In this paper, we present a flexible approach to the efficient and exhaustive manual annotation of text documents. For this purpose, we extend WebAnno (, 2013) an open-source web-based annotation tool. 1 While it was previously limited to specific annotation layers, our extension allows adding and configuring an arbitrary number of layers through a web-based UI. These layers can be annotated separately or simultaneously, and support most types of linguistic annotations such as spans, semantic classes, dependency relations, lexical chains, and morphology. Further, we tightly integrate a generic machine learning component for automatic annotation suggestions of span annotations. In two case studies, we show that automatic annotation suggestions, combined with our split-pane UI concept, significantly reduces annotation time. A general-purpose text annotation tool called Knowtator is introduced. Knowtator facilitates the manual creation of annotated corpora that can be used for evaluating or training a variety of natural language processing systems. Building on the strengths of the widely used Protege knowledge representation system, Knowtator has been developed as a Protege plug-in that leverages Protege's knowledge representation capabilities to specify annotation schemas. Knowtator's unique advantage over other annotation tools is the ease with which complex annotation schemas (e.g. schemas which have constrained relationships between annotation types) can be defined and incorporated into use. Knowtator is available under the Mozilla Public License 1.1 at http: bionlp.sourceforge.net Knowtator. Millions of learners today use how-to videos to master new skills in a variety of domains. But browsing such videos is often tedious and inefficient because video player interfaces are not optimized for the unique step-by-step structure of such videos. This research aims to improve the learning experience of existing how-to videos with step-by-step annotations. We first performed a formative study to verify that annotations are actually useful to learners. We created ToolScape, an interactive video player that displays step descriptions and intermediate result thumbnails in the video timeline. Learners in our study performed better and gained more self-efficacy using ToolScape versus a traditional video player. To add the needed step annotations to existing how-to videos at scale, we introduce a novel crowdsourcing workflow. It extracts step-by-step structure from an existing video, including step times, descriptions, and before and after images. We introduce the Find-Verify-Expand design pattern for temporal and visual annotation, which applies clustering, text processing, and visual analysis algorithms to merge crowd output. The workflow does not rely on domain-specific customization, works on top of existing videos, and recruits untrained crowd workers. We evaluated the workflow with Mechanical Turk, using 75 cooking, makeup, and Photoshop videos on YouTube. Results show that our workflow can extract steps with a quality comparable to that of trained annotators across all three domains with 77 precision and 81 recall. We introduce the brat rapid annotation tool (BRAT), an intuitive web-based tool for text annotation supported by Natural Language Processing (NLP) technology. BRAT has been developed for rich structured annotation for a variety of NLP tasks and aims to support manual curation efforts and increase annotator productivity using NLP techniques. We discuss several case studies of real-world annotation projects using pre-release versions of BRAT and present an evaluation of annotation assisted by semantic class disambiguation on a multicategory entity mention annotation task, showing a 15 decrease in total annotation time. BRAT is available under an open-source license from: http: brat.nlplab.org