This paper introduces a new web-based software tool for annotating text, Text Annotation Graphs, or TAG. It provides functionality for representing complex relationships between words and word phrases that are not available in other software tools, including the ability to define and visualize relationships between the relationships themselves (semantic hypergraphs). Additionally, we include an approach to representing text annotations in which annotation subgraphs, or semantic summaries, are used to show relationships outside of the sequential context of the text itself. Users can use these subgraphs to quickly find similar structures within the current document or external annotated documents. Initially, TAG was developed to support information extraction tasks on a large database of biomedical articles. However, our software is flexible enough to support a wide range of annotation tasks for any domain. Examples are provided that showcase TAG's capabilities on morphological parsing and event extraction tasks.
is a flexible tool that supports multiple annotation layers, and includes features to facilitate quality control, annotator management, and curation @cite_8 @cite_16 . The visualrepresentation is similar to , and the interface focuses mainly on resolving disagreeing annotations between users. includes a variety of built-in annotation layers, such as dependency relations, co-reference chains, and lemma forms, but in annotation expressiveness is limited in that it is not possible to create nested arcs that link to other arcs.
@cite_16 @cite_8
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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. Automatic image annotation systems available in the literature concatenate color, texture and or shape features in a single feature vector to learn a set of high level semantic categories using a single learning machine. This approach is quite naive to map the visual features to high level semantic information concerning the categories. Concatenation of many features with different visual properties and wide dynamical ranges may result in curse of dimensionality and redundancy problems. Additionally, it usually requires normalization which may cause an undesirable distortion in the feature space. An elegant way of reducing the effects of these problems is to design a dedicated feature space for each image category, depending on its content, and learn a range of visual properties of the whole image from a variety of feature sets. For this purpose, a two-layer ensemble learning system, called Supervised Annotation by Descriptor Ensemble (SADE), is proposed. SADE, initially, extracts a variety of low-level visual descriptors from the image. Each descriptor is, then, fed to a separate learning machine in the first layer. Finally, the meta-layer classifier is trained on the output of the first layer classifiers and the images are annotated by using the decision of the meta-layer classifier. This approach not only avoids normalization, but also reduces the effects of dimensional curse and redundancy. The proposed system outperforms a state-of-the-art automatic image annotation system, in an equivalent experimental setup. 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. RGBD images with high quality annotations, both in the form of geometric i.e., segmentation and structural i.e., how do the segments mutually relate in 3D information, provide valuable priors for a diverse range of applications in scene understanding and image manipulation. While it is now simple to acquire RGBD images, annotating them, automatically or manually, remains challenging. We present SmartAnnotator, an interactive system to facilitate annotating raw RGBD images. The system performs the tedious tasks of grouping pixels, creating potential abstracted cuboids, inferring object interactions in 3D, and generates an ordered list of hypotheses. The user simply has to flip through the suggestions for segment labels, finalize a selection, and the system updates the remaining hypotheses. As annotations are finalized, the process becomes simpler with fewer ambiguities to resolve. Moreover, as more scenes are annotated, the system makes better suggestions based on the structural and geometric priors learned from previous annotation sessions. We test the system on a large number of indoor scenes across different users and experimental settings, validate the results on existing benchmark datasets, and report significant improvements over low-level annotation alternatives. Code and benchmark datasets are publicly available on the project page.