Related papers: SLATE: A Super-Lightweight Annotation Tool for Exp…
We present a lightweight annotation tool, the Data AnnotatoR Tool (DART), for the general task of labeling structured data with textual descriptions. The tool is implemented as an interactive application that reduces human efforts in…
In this paper, we introduce \textsc{Yedda}, a lightweight but efficient and comprehensive open-source tool for text span annotation. \textsc{Yedda} provides a systematic solution for text span annotation, ranging from collaborative user…
We present POTATO, the Portable text annotation tool, a free, fully open-sourced annotation system that 1) supports labeling many types of text and multimodal data; 2) offers easy-to-configure features to maximize the productivity of both…
The construction of high-quality parallel corpora for translation research has increasingly evolved from simple sentence alignment to complex, multi-layered annotation tasks. This methodological shift presents significant challenges for…
Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to…
Text annotation tools assume that their user's goal is to create a labeled corpus. However, users view annotation as a necessary evil on the way to deliver business value through NLP. Thus an annotation tool should optimize for the…
Recent research in the field of computer vision strongly focuses on deep learning architectures to tackle image processing problems. Deep neural networks are often considered in complex image processing scenarios since traditional computer…
The Annotation Graph Toolkit is a collection of software supporting the development of annotation tools based on the annotation graph model. The toolkit includes application programming interfaces for manipulating annotation graph data and…
Without well-labeled ground truth data, machine learning-based systems would not be as ubiquitous as they are today, but these systems rely on substantial amounts of correctly labeled data. Unfortunately, crowdsourced labeling is time…
In this work, we present SenTag, a lightweight web-based tool focused on semantic annotation of textual documents. The platform allows multiple users to work on a corpus of documents. The tool enables to tag a corpus of documents through an…
Fine-grained Entity Typing is a tough task which suffers from noise samples extracted from distant supervision. Thousands of manually annotated samples can achieve greater performance than millions of samples generated by the previous…
We introduce Fluid Annotation, an intuitive human-machine collaboration interface for annotating the class label and outline of every object and background region in an image. Fluid annotation is based on three principles: (I) Strong…
Creating linguistic annotations requires more than just a reliable annotation scheme. Annotation can be a complex endeavour potentially involving many people, stages, and tools. This chapter outlines the process of creating end-to-end…
Annotation tools are the starting point for creating Natural Language Processing (NLP) datasets. There is a wide variety of tools available; setting up these tools is however a hindrance. We propose EEVEE, an annotation tool focused on…
We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or "inked") notes on a virtual whiteboard. Our approach allows us to create a single, low-latency model to…
Tag-Pag is an application designed to simplify the categorization of web pages, a task increasingly common for researchers who scrape web pages to analyze individuals' browsing patterns or train machine learning classifiers. Unlike existing…
LiDAR (Light Detection And Ranging) is an essential and widely adopted sensor for autonomous vehicles, particularly for those vehicles operating at higher levels (L4-L5) of autonomy. Recent work has demonstrated the promise of deep-learning…
Many existing systems track aliasing and uniqueness, each with their own trade-off between expressiveness and developer effort. We propose Latte, a new approach that aims to minimize both the amount of annotations and the complexity of…
We introduce a framework for lightweight dependency syntax annotation. Our formalism builds upon the typical representation for unlabeled dependencies, permitting a simple notation and annotation workflow. Moreover, the formalism encourages…
Manual annotation of textual documents is a necessary task when constructing benchmark corpora for training and evaluating machine learning algorithms. We created a comprehensive directory of annotation tools that currently includes 93…