Related papers: Annotation as a New Paradigm in Research Archiving

We describe a formal model for annotating linguistic artifacts, from which we derive an application programming interface (API) to a suite of tools for manipulating these annotations. The abstract logical model provides for a range of…

Text embeddings are central to modern information retrieval and Retrieval-Augmented Generation (RAG). While dense models derived from Large Language Models (LLMs) dominate current practice, recent work has explored quantum-inspired…

Prevalent efforts have been put in automatically inferring genres of musical items. Yet, the propose solutions often rely on simplifications and fail to address the diversity and subjectivity of music genres. Accounting for these has,…

Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel…

We present a comprehensive survey on the use of annotations in information visualizations, highlighting their crucial role in improving audience understanding and engagement with visual data. Our investigation encompasses empirical studies…

Distant viewing approaches have typically used image datasets close to the contemporary image data used to train machine learning models. To work with images from other historical periods requires expert annotated data, and the quality of…

This article proposes a novel methodological approach for developing use cases for CH e-infrastuctures documented using Jupyter Notebooks (JNs), enabling transparency and reproducibility. We also address the present problem of use cases…

In this paper, we present a novel annotation approach to capture claims and premises of arguments and their relations in student-written persuasive peer reviews on business models in German language. We propose an annotation scheme based on…

Finding online research papers relevant to one's interests is very challenging due to the increasing number of publications. Therefore, personalized research paper recommendation has become a significant and timely research topic.…

Annotation is the labeling of data by human effort. Annotation is critical to modern machine learning, and Bloomberg has developed years of experience of annotation at scale. This report captures a wealth of wisdom for applied annotation…

Large Language Models (LLMs) have ushered in a new era of text annotation, as their ease-of-use, high accuracy, and relatively low costs have meant that their use has exploded in recent months. However, the rapid growth of the field has…

The aim of this paper is to propose an approach based on the concept of annotation for supporting design communication. In this paper, we describe a co-operative design case study where we analyse some annotation practices, mainly focused…

Hebrew is a Morphological rich language, making its modeling harder than simpler language. Recent developments such as Transformers in general and Bert in particular opened a path for Hebrew models that reach SOTA results, not falling short…

Along the design process of interactive system many intermediate artefacts (such as user interface prototypes, task models describing user work and activities, dialog models specifying system behavior, interaction models describing user…

Active learning, a label-efficient paradigm, empowers models to interactively query an oracle for labeling new data. In the realm of LiDAR semantic segmentation, the challenges stem from the sheer volume of point clouds, rendering…

This paper is concerned with tracking and interpreting scholarly documents in distributed research communities. We argue that current approaches to document description, and current technological infrastructures particularly over the World…

Large amounts of annotated data have become more important than ever, especially since the rise of deep learning techniques. However, manual annotations are costly. We propose a tool that enables researchers to create large, high-quality,…

Crowdsourcing platforms use various truth discovery algorithms to aggregate annotations from multiple labelers. In an online setting, however, the main challenge is to decide whether to ask for more annotations for each item to efficiently…

Efficient document retrieval heavily relies on the technique of semantic hashing, which learns a binary code for every document and employs Hamming distance to evaluate document distances. However, existing semantic hashing methods are…

We propose an annotation approach that captures not only labels but also the reading process underlying annotators' decisions, e.g., what parts of the text they focus on, re-read or skim. Using this framework, we conduct a case study on the…