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Journal articles on the topic 'Manual annotation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Zenonas, Theodosiou, and Tsapatsoulis Nicolas. "Image annotation: the effects of content, lexicon and annotation method." International Journal of Multimedia Information Retrieval 9 (March 1, 2020): 191–203. https://doi.org/10.1007/s13735-020-00193-z.

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Image annotation is the process of assigning metadata to images, allowing effective retrieval by text-based search techniques. Despite the lots of eorts in automatic multimedia analysis, automatic semantic annotation of multimedia is still inefficient due to the problems in modelling high level semantic terms. In this paper we examine the factors affecting the quality of annotations collected through crowdsourcing platforms. An image dataset was manually annotated utilizing: (i) a vocabulary consists of pre-selected set of keywords,(ii) an hierarchical vocabulary, and (iii) free keywords. The

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Hecksel, Corey W., Michele C. Darrow, Wei Dai, et al. "Quantifying Variability of Manual Annotation in Cryo-Electron Tomograms." Microscopy and Microanalysis 22, no. 3 (2016): 487–96. http://dx.doi.org/10.1017/s1431927616000799.

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AbstractAlthough acknowledged to be variable and subjective, manual annotation of cryo-electron tomography data is commonly used to answer structural questions and to create a “ground truth” for evaluation of automated segmentation algorithms. Validation of such annotation is lacking, but is critical for understanding the reproducibility of manual annotations. Here, we used voxel-based similarity scores for a variety of specimens, ranging in complexity and segmented by several annotators, to quantify the variation among their annotations. In addition, we have identified procedures for merging

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Rele, Chinmay P., Katie M. Sandlin, Wilson Leung, and Laura K. Reed. "Manual annotation of Drosophila genes: a Genomics Education Partnership protocol." F1000Research 11 (December 23, 2022): 1579. http://dx.doi.org/10.12688/f1000research.126839.1.

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Annotating the genomes of multiple species allows us to analyze the evolution of their genes. While many eukaryotic genome assemblies already include computational gene predictions, these predictions can benefit from review and refinement through manual gene annotation. The Genomics Education Partnership (GEP; https://thegep.org/) developed a structural annotation protocol for protein-coding genes that enables undergraduate student and faculty researchers to create high-quality gene annotations that can be utilized in subsequent scientific investigations. For example, this protocol has been ut

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Rele, Chinmay P., Katie M. Sandlin, Wilson Leung, and Laura K. Reed. "Manual annotation of Drosophila genes: a Genomics Education Partnership protocol." F1000Research 11 (October 13, 2023): 1579. http://dx.doi.org/10.12688/f1000research.126839.3.

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Annotating the genomes of multiple species allows us to analyze the evolution of their genes. While many eukaryotic genome assemblies already include computational gene predictions, these predictions can benefit from review and refinement through manual gene annotation. The Genomics Education Partnership (GEP; https://thegep.org/) developed a structural annotation protocol for protein-coding genes that enables undergraduate student and faculty researchers to create high-quality gene annotations that can be utilized in subsequent scientific investigations. For example, this protocol has been ut

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Rele, Chinmay P., Katie M. Sandlin, Wilson Leung, and Laura K. Reed. "Manual annotation of Drosophila genes: a Genomics Education Partnership protocol." F1000Research 11 (July 31, 2023): 1579. http://dx.doi.org/10.12688/f1000research.126839.2.

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Annotating the genomes of multiple species allows us to analyze the evolution of their genes. While many eukaryotic genome assemblies already include computational gene predictions, these predictions can benefit from review and refinement through manual gene annotation. The Genomics Education Partnership (GEP; https://thegep.org/) developed a structural annotation protocol for protein-coding genes that enables undergraduate student and faculty researchers to create high-quality gene annotations that can be utilized in subsequent scientific investigations. For example, this protocol has been ut

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Lu, Xiaolu, David Ratcliffe, Tsu-Ting Kao, et al. "Rethinking Quality Assurance for Crowdsourced Multi-ROI Image Segmentation." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 11, no. 1 (2023): 103–14. http://dx.doi.org/10.1609/hcomp.v11i1.27552.

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Collecting high quality annotations to construct an evaluation dataset is essential for assessing the true performance of machine learning models. One popular way of performing data annotation is via crowdsourcing, where quality can be of concern. Despite much prior work addressing the annotation quality problem in crowdsourcing generally, little has been discussed in detail for image segmentation tasks. These tasks often require pixel-level annotation accuracy, and is relatively complex when compared to image classification or object detection with bounding-boxes. In this paper, we focus on i

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Finlayson, Mark. "The Story Workbench: An Extensible Semi-Automatic Text Annotation Tool." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 7, no. 2 (2011): 21–24. http://dx.doi.org/10.1609/aiide.v7i2.12458.

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Text annotations are of great use to researchers in the language sciences, and much effort has been invested in creating annotated corpora for an wide variety of purposes. Unfortunately, software support for these corpora tends to be quite limited: it is usually ad-hoc, poorly designed and documented, or not released for public use. I describe an annotation tool, the Story Workbench, which provides a generic platform for text annotation. It is free, open-source, cross-platform, and user friendly. It provides a number of common text annotation operations, including representations (e.g., tokens

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Rasmussen, Christoffer Bøgelund, Kristian Kirk, and Thomas B. Moeslund. "The Challenge of Data Annotation in Deep Learning—A Case Study on Whole Plant Corn Silage." Sensors 22, no. 4 (2022): 1596. http://dx.doi.org/10.3390/s22041596.

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Recent advances in computer vision are primarily driven by the usage of deep learning, which is known to require large amounts of data, and creating datasets for this purpose is not a trivial task. Larger benchmark datasets often have detailed processes with multiple stages and users with different roles during annotation. However, this can be difficult to implement in smaller projects where resources can be limited. Therefore, in this work we present our processes for creating an image dataset for kernel fragmentation and stover overlengths in Whole Plant Corn Silage. This includes the guidel

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Ahmed, Saad, Saman Hina, Raheela Asif, Sana Ahmed, and Munad Ahmed. "Development and Evaluation of Gold Standard Dataset for Sentiment Analysis of Tweets." Pakistan Journal of Engineering and Technology 6, no. 4 (2024): 7–12. http://dx.doi.org/10.51846/vol6iss4pp7-12.

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Pre-labeled data is typically required for supervised machine learning. A limited number of object classes in the majority of open access and pre-annotated datasets make them unsuitable for certain tasks, even though they are readily available for training machine learning algorithms. For custom models, previously available pre-annotated data is typically insufficient, so gathering and preparing training data is necessary for the majority of real-world applications. The quantity and quality of annotations clearly trade-off with one another. Either more annotated data can be produced or better

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Benitez-Garcia, Gibran, Jesus Olivares-Mercado, Gabriel Sanchez-Perez, and Hiroki Takahashi. "IPN HandS: Efficient Annotation Tool and Dataset for Skeleton-Based Hand Gesture Recognition." Applied Sciences 15, no. 11 (2025): 6321. https://doi.org/10.3390/app15116321.

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Hand gesture recognition (HGR) heavily relies on high-quality annotated datasets. However, annotating hand landmarks in video sequences is a time-intensive challenge. In this work, we introduce IPN HandS, an enhanced version of our IPN Hand dataset, which now includes approximately 700,000 hand skeleton annotations and corrected gesture boundaries. To generate these annotations efficiently, we propose a novel annotation tool that combines automatic detection, inter-frame interpolation, copy–paste capabilities, and manual refinement. This tool significantly reduces annotation time from 70 min t

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Vădineanu, Serban, Daniël M. Pelt, Oleh Dzyubachyk, and Kees Joost Batenburg. "Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations." Journal of Imaging 10, no. 7 (2024): 172. http://dx.doi.org/10.3390/jimaging10070172.

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Deep-learning algorithms for cell segmentation typically require large data sets with high-quality annotations to be trained with. However, the annotation cost for obtaining such sets may prove to be prohibitively expensive. Our work aims to reduce the time necessary to create high-quality annotations of cell images by using a relatively small well-annotated data set for training a convolutional neural network to upgrade lower-quality annotations, produced at lower annotation costs. We investigate the performance of our solution when upgrading the annotation quality for labels affected by thre

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Kors, Jan A., Simon Clematide, Saber A. Akhondi, Erik M. van Mulligen, and Dietrich Rebholz-Schuhmann. "A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC." Journal of the American Medical Informatics Association 22, no. 5 (2015): 948–56. http://dx.doi.org/10.1093/jamia/ocv037.

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Abstract Objective To create a multilingual gold-standard corpus for biomedical concept recognition. Materials and methods We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups. To reduce the annotation workload, automatically generated preannotations were provided. Individual annotations were automatica

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Cronkite, David, Bradley Malin, John Aberdeen, Lynette Hirschman, and David Carrell. "Is the Juice Worth the Squeeze? Costs and Benefits of Multiple Human Annotators for Clinical Text De-identification." Methods of Information in Medicine 55, no. 04 (2016): 356–64. http://dx.doi.org/10.3414/me15-01-0122.

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SummaryBackground: Clinical text contains valuable information but must be de-identified before it can be used for secondary purposes. Accurate annotation of personally identifiable information (PII) is essential to the development of automated de-identification systems and to manual redaction of PII. Yet the accuracy of annotations may vary considerably across individual annotators and annotation is costly. As such, the marginal benefit of incorporating additional annotators has not been well characterized.Objectives: This study models the costs and benefits of incorporating increasing number

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Fuoli, Matteo. "A stepwise method for annotating appraisal." Functions of Language 25, no. 2 (2018): 229–58. http://dx.doi.org/10.1075/fol.15016.fuo.

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Abstract Despite a growing awareness of methodological issues, the literature on appraisal has not so far provided adequate answers to some of the key challenges involved in reliably identifying and classifying evaluative language expressions. This article presents a stepwise method for the manual annotation of appraisal in text that is designed to optimize reliability, replicability and transparency. The procedure consists of seven steps, from the creation of a context-specific annotation manual to the statistical analysis of the quantitative data derived from the manually-performed annotatio

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Bayerl, Petra Saskia, and Karsten Ingmar Paul. "Identifying Sources of Disagreement: Generalizability Theory in Manual Annotation Studies." Computational Linguistics 33, no. 1 (2007): 3–8. http://dx.doi.org/10.1162/coli.2007.33.1.3.

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Many annotation projects have shown that the quality of manual annotations often is not as good as would be desirable for reliable data analysis. Identifying the main sources responsible for poor annotation quality must thus be a major concern. Generalizability theory is a valuable tool for this purpose, because it allows for the differentiation and detailed analysis of factors that influence annotation quality. In this article we will present basic concepts of Generalizability Theory and give an example for its application based on published data.

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Bayerl, Petra Saskia, and Karsten Ingmar Paul. "What Determines Inter-Coder Agreement in Manual Annotations? A Meta-Analytic Investigation." Computational Linguistics 37, no. 4 (2011): 699–725. http://dx.doi.org/10.1162/coli_a_00074.

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Recent discussions of annotator agreement have mostly centered around its calculation and interpretation, and the correct choice of indices. Although these discussions are important, they only consider the “back-end” of the story, namely, what to do once the data are collected. Just as important in our opinion is to know how agreement is reached in the first place and what factors influence coder agreement as part of the annotation process or setting, as this knowledge can provide concrete guidelines for the planning and set-up of annotation projects. To investigate whether there are factors t

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Salisbury, Alicia, and Philippos K. Tsourkas. "A Method for Improving the Accuracy and Efficiency of Bacteriophage Genome Annotation." International Journal of Molecular Sciences 20, no. 14 (2019): 3391. http://dx.doi.org/10.3390/ijms20143391.

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Bacteriophages are the most numerous entities on Earth. The number of sequenced phage genomes is approximately 8000 and increasing rapidly. Sequencing of a genome is followed by annotation, where genes, start codons, and functions are putatively identified. The mainstays of phage genome annotation are auto-annotation programs such as Glimmer and GeneMark. Due to the relatively small size of phage genomes, many groups choose to manually curate auto-annotation results to increase accuracy. An additional benefit of manual curation of auto-annotated phage genomes is that the process is amenable to

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VanBerlo, Bennett, Delaney Smith, Jared Tschirhart, et al. "Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View." Diagnostics 12, no. 10 (2022): 2351. http://dx.doi.org/10.3390/diagnostics12102351.

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Background: Annotating large medical imaging datasets is an arduous and expensive task, especially when the datasets in question are not organized according to deep learning goals. Here, we propose a method that exploits the hierarchical organization of annotating tasks to optimize efficiency. Methods: We trained a machine learning model to accurately distinguish between one of two classes of lung ultrasound (LUS) views using 2908 clips from a larger dataset. Partitioning the remaining dataset by view would reduce downstream labelling efforts by enabling annotators to focus on annotating patho

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De Joode, Johan. "Problem-oriented Corpus Annotation and the Hebrew Bible." HIPHIL Novum 5, no. 2 (2019): 6–12. http://dx.doi.org/10.7146/hn.v5i2.142730.

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In this contribution, I argue that the exegetical and stylistic study of the Hebrew Bible would benefit from the creation and storage of qualitative and quantitative annotations using problem-oriented corpus annotation (de Haan 1984). Within Biblical studies exegetes are used to static interfaces which allow them retrieve information, but not enhance it with anything more elaborate than user notes. I present a roadmap for the development of an annotation tool tailored to the Hebrew Bible with the sole objective of enriching the data that is already present in open source datasets like that of

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Culot, Antoine, Guillaume Abriat, and Kieran P. Furlong. "High-Performance Genome Annotation for a Safer and Faster-Developing Phage Therapy." Viruses 17, no. 3 (2025): 314. https://doi.org/10.3390/v17030314.

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Phage therapy, which uses phages to decrease bacterial load in an ecosystem, introduces a multitude of gene copies (bacterial and phage) into said ecosystem. While it is widely accepted that phages have a significant impact on ecology, the mechanisms underlying their impact are not well understood. It is therefore paramount to understand what is released in the said ecosystem, to avoid alterations with difficult-to-predict—but potentially huge—consequences. An in-depth annotation of therapeutic phage genomes is therefore essential. Currently, the average published phage genome has only 20–30%

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Aulamo, Mikko, Mathias Creutz, and Eetu Sjöblom. "Annotation of subtitle paraphrases using a new web tool." Digital Humanities in the Nordic and Baltic Countries Publications 2, no. 1 (2019): 33–48. http://dx.doi.org/10.5617/dhnbpub.11021.

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This paper analyzes the manual annotation effort carried out to produce Opusparcus, the Open Subtitles Paraphrase Corpus for six European languages. Within the scope of the project, a new web-based annotation tool was created. We discuss the design choices behind the tool as well as the setup of the annotation task. We also evaluate the annotations obtained. Two independent annotators needed to decide to what extent two sentences approximately meant the same thing. The sentences originate from subtitles from movies and TV shows, which constitutes an interesting genre of mostly colloquial langu

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Skovgaard, Esben Lykke, Jesper Pedersen, Niels Christian Møller, Anders Grøntved, and Jan Christian Brønd. "Manual Annotation of Time in Bed Using Free-Living Recordings of Accelerometry Data." Sensors 21, no. 24 (2021): 8442. http://dx.doi.org/10.3390/s21248442.

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With the emergence of machine learning for the classification of sleep and other human behaviors from accelerometer data, the need for correctly annotated data is higher than ever. We present and evaluate a novel method for the manual annotation of in-bed periods in accelerometer data using the open-source software Audacity®, and we compare the method to the EEG-based sleep monitoring device Zmachine® Insight+ and self-reported sleep diaries. For evaluating the manual annotation method, we calculated the inter- and intra-rater agreement and agreement with Zmachine and sleep diaries using inter

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Lertpoompunya, Angkana, Nathan C. Higgins, Erol J. Ozmeral, and David A. Eddins. "Head movement during natural group conversation and inter-annotator agreement on manual annotation." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A111—A112. http://dx.doi.org/10.1121/10.0022958.

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During speech communication and conversational turn-taking, listeners direct their head and eyes to receive meaningful auditory and visual cues. Features of these behaviors may convey listener intent. This study designed a test environment, data collection protocol and procedures, and investigated head movement behaviors during self-driven conversations among multiple partners. Nine participants were tested in cohorts of three. Participants wore a headset with sensors tracked by an infrared camera system. Participants watched an audio-video clip, followed by a 5-min undirected discussion. The

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Wu, Xian, Wei Fan, and Yong Yu. "Sembler: Ensembling Crowd Sequential Labeling for Improved Quality." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1713–19. http://dx.doi.org/10.1609/aaai.v26i1.8351.

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Many natural language processing tasks, such as named entity recognition (NER), part of speech (POS) tagging, word segmentation, and etc., can be formulated as sequential data labeling problems. Building a sound labeler requires very large number of correctly labeled training examples, which may not always be possible. On the other hand, crowdsourcing provides an inexpensive yet efficient alternative to collect manual sequential labeling from non-experts. However the quality of crowd labeling cannot be guaranteed, and three kinds of errors are typical: (1) incorrect annotations due to lack of

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Žejn, Andrejka, and Mojca Šorli. "Named Entities in Modernist Literary Texts." Slovenščina 2.0: empirical applied and interdisciplinary research 11, no. 1 (2023): 118–37. http://dx.doi.org/10.4312/slo2.0.2023.1.118-137.

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This paper is a follow-up and elaboration of the paper published in the JTDH 2022 Conference Proceedings on manual semantic annotation of named entities based on a proposed set of annotations for a corpus of modernist literary texts. We first briefly describe the corpus and introduce the annotation scheme, then focus on the results of additional analyses, and conclude with further challenges and issues we identified with respect to established NER systems and practices of related projects. Overall, we identify several categories of proper names, foreign language elements, and bibliographic cit

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Gil de Gómez Pérez, David, and Roman Bednarik. "POnline: An Online Pupil Annotation Tool Employing Crowd-sourcing and Engagement Mechanisms." Human Computation 6 (December 10, 2019): 176–91. http://dx.doi.org/10.15346/hc.v6i1.99.

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Pupil center and pupil contour are two of the most important features in the eye-image used for video-based eye-tracking. Well annotated databases are needed in order to allow benchmarking of the available- and new pupil detection and gaze estimation algorithms. Unfortunately, creation of such a data set is costly and requires a lot of efforts, including manual work of the annotators. In addition, reliability of manual annotations is hard to establish with a low number of annotators. In order to facilitate progress of the gaze tracking algorithm research, we created an online pupil annotation

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Fabeela, Ali Rawther Abhinay A. K. Anagha Tess B. Alan Joseph Adham Saheer. "Evaluating Annotation Consistency in Offensive Language Detection: A Data Analytics Approach on the TweetEval Dataset." International Journal on Emerging Research Areas (IJERA) 05, no. 01 (2025): 202–5. https://doi.org/10.5281/zenodo.15532596.

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<strong><em>Abstract</em></strong><strong>— Most machine learning models are not only highly dependent on difficult datasets but also on the quality of labeled data they are trained on, especially for offensive content detection. In this paper, we study the TweetEval dataset to provide a comparison of its ground truth with manually annotated labels; inter-annotator agreements are applied here as a metric for assessing the consistency of annotation. Cohen’s Kappa coefficient is used to quantify how much each pair of annotators agreed and where they differed. In-depth examination of

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FERNÁNDEZ, N., J. A. FISTEUS, D. FUENTES, L. SÁNCHEZ, and V. LUQUE. "A WIKIPEDIA-BASED FRAMEWORK FOR COLLABORATIVE SEMANTIC ANNOTATION." International Journal on Artificial Intelligence Tools 20, no. 05 (2011): 847–86. http://dx.doi.org/10.1142/s0218213011000413.

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The semantic web aims at automating web data processing tasks that nowadays only humans are able to do. To make this vision a reality, the information on web resources should be described in a computer-meaningful way, in a process known as semantic annotation. In this paper, a manual, collaborative semantic annotation framework is described. It is designed to take advantage of the benefits of manual annotation systems (like the possibility of annotating formats difficult to annotate in an automatic manner) addressing at the same time some of their limitations (reduce the burden for non-expert

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Hao, Tianyong, Chunshen Zhu, Yuanyuan Mu, and Gang Liu. "A user-oriented semantic annotation approach to knowledge acquisition and conversion." Journal of Information Science 43, no. 3 (2016): 393–411. http://dx.doi.org/10.1177/0165551516642688.

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Semantic annotation on natural language texts labels the meaning of an annotated element in specific contexts, and thus is an essential procedure for domain knowledge acquisition. An extensible and coherent annotation method is crucial for knowledge engineers to reduce human efforts to keep annotations consistent. This article proposes a comprehensive semantic annotation approach supported by a user-oriented markup language named UOML to enhance annotation efficiency with the aim of building a high quality knowledge base. UOML is operable by human annotators and convertible to formal knowledge

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Wood, Valerie, Seth Carbon, Midori A. Harris, et al. "Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns." Open Biology 10, no. 9 (2020): 200149. http://dx.doi.org/10.1098/rsob.200149.

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Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotati

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Steier, Janik, Mona Goebel, and Dorota Iwaszczuk. "Is Your Training Data Really Ground Truth? A Quality Assessment of Manual Annotation for Individual Tree Crown Delineation." Remote Sensing 16, no. 15 (2024): 2786. http://dx.doi.org/10.3390/rs16152786.

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For the accurate and automatic mapping of forest stands based on very-high-resolution satellite imagery and digital orthophotos, precise object detection at the individual tree level is necessary. Currently, supervised deep learning models are primarily applied for this task. To train a reliable model, it is crucial to have an accurate tree crown annotation dataset. The current method of generating these training datasets still relies on manual annotation and labeling. Because of the intricate contours of tree crowns, vegetation density in natural forests and the insufficient ground sampling d

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Klubička, Filip, Antonio Toral, and Víctor M. Sánchez-Cartagena. "Fine-Grained Human Evaluation of Neural Versus Phrase-Based Machine Translation." Prague Bulletin of Mathematical Linguistics 108, no. 1 (2017): 121–32. http://dx.doi.org/10.1515/pralin-2017-0014.

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AbstractWe compare three approaches to statistical machine translation (pure phrase-based, factored phrase-based and neural) by performing a fine-grained manual evaluation via error annotation of the systems’ outputs. The error types in our annotation are compliant with the multidimensional quality metrics (MQM), and the annotation is performed by two annotators. Inter-annotator agreement is high for such a task, and results show that the best performing system (neural) reduces the errors produced by the worst system (phrase-based) by 54%.

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McDowell, Jillian Marie, Gillian Margaret Johnson, and Barbara Helen Hetherington. "Mulligan Concept manual therapy: Standardizing annotation." Manual Therapy 19, no. 5 (2014): 499–503. http://dx.doi.org/10.1016/j.math.2013.12.006.

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Wei, Shuangfeng, Hongrui Tang, Changchang Liu, et al. "DeepLabV3+-Based Semantic Annotation Refinement for SLAM in Indoor Environments." Sensors 25, no. 11 (2025): 3344. https://doi.org/10.3390/s25113344.

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Visual SLAM systems frequently encounter challenges in accurately reconstructing three-dimensional scenes from monocular imagery in semantically deficient environments, which significantly compromises robotic operational efficiency. While conventional manual annotation approaches can provide supplemental semantic information, they are inherently inefficient, procedurally complex, and labor-intensive. This paper presents an optimized DeepLabV3+-based framework for visual SLAM that integrates image semantic segmentation with automated point cloud semantic annotation. The proposed method utilizes

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Li, Jiabin, Tingxin Wei, Weiguang Qu, Bin Li, Minxuan Feng, and Dongbo Wang. "Combining Lexicon Definitions and the Retrieval-Augmented Generation of a Large Language Model for the Automatic Annotation of Ancient Chinese Poetry." Mathematics 13, no. 12 (2025): 2023. https://doi.org/10.3390/math13122023.

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Existing approaches to the automatic annotation of classical Chinese poetry often fail to generate precise source citations and depend heavily on manual segmentation, limiting their scalability and accuracy. To address these shortcomings, we propose a novel paradigm that integrates dictionary retrieval with retrieval-augmented large language model enhancements for automatic poetic annotation. Our method leverages the contextual understanding capabilities of large models to dynamically select appropriate lexical senses and employs an automated segmentation technique to minimize reliance on manu

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O'Connor, Karen, Abeed Sarker, Jeanmarie Perrone, and Graciela Gonzalez Hernandez. "Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and Guidelines." Journal of Medical Internet Research 22, no. 2 (2020): e15861. http://dx.doi.org/10.2196/15861.

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Background Social media data are being increasingly used for population-level health research because it provides near real-time access to large volumes of consumer-generated data. Recently, a number of studies have explored the possibility of using social media data, such as from Twitter, for monitoring prescription medication abuse. However, there is a paucity of annotated data or guidelines for data characterization that discuss how information related to abuse-prone medications is presented on Twitter. Objective This study discusses the creation of an annotated corpus suitable for training

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Novák, Václav. "Semantic Network Manual Annotation and its Evaluation." Prague Bulletin of Mathematical Linguistics 90, no. 1 (2008): 69–82. http://dx.doi.org/10.2478/v10108-009-0008-4.

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Semantic Network Manual Annotation and its Evaluation The present contribution is a brief extract of (Novák, 2008). The Prague Dependency Treebank (PDT) is a valuable resource of linguistic information annotated on several layers. These layers range from morphemic to deep and they should contain all the linguistic information about the text. The natural extension is to add a semantic layer suitable as a knowledge base for tasks like question answering, information extraction etc. In this paper I set up criteria for this representation, explore the possible formalisms for this task and discuss

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Talamo, Luigi, Chiara Celata, and Pier Marco Bertinetto. "DerIvaTario: An annotated lexicon of Italian derivatives." Word Structure 9, no. 1 (2016): 72–102. http://dx.doi.org/10.3366/word.2016.0087.

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We propose an annotation schema for derivational morphology featuring morphological, morphotactic and morphosemantic information concerning the base of the derivative as well as each derivational cycle. This schema was employed in the manual annotation of about 11,000 Italian derivatives, extracted from the CoLFIS corpus. The outcome is DerIvaTario, an annotated lexicon of Italian derivatives. The inter-annotator agreement was assessed over several variables of the annotation schema. DerIvaTario is available as an interactive database to be used for theoretical morphology and psycholinguistic

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Lagier, Michael J., Brittany Bowman, Kelsey Brend, Katherine Hobbs, Michael Foggia, and Mark McDaniel. "Improved Functional Prediction of Hypothetical Proteins from Listeria monocytogenes 08-5578." Journal of the Iowa Academy of Science 121, no. 1-4 (2014): 16–27. http://dx.doi.org/10.17833/121-03.1.

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Listeria monocytogenes is a foodborne human pathogen responsible for listerosis. The genomes of several L. monocytogenes strains have been recently sequenced. The genome of L. monocytogenes 08-5578, which was in part responsible for a significant listerosis outbreak in 2008, contains an unexpectedly high percentage of protein-encoding genes (1,927 out of 3,161; 60.96%) autonomously annotated as hypothetical proteins. The aim of this study was to test whether a manual annotation strategy could be used to assign more meaningful functional names to the hypothetical proteins of 08-5578. A holistic

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Mohd, Mudasir, Rafiya Jan, and Nida Hakak. "Enhanced Bootstrapping Algorithm for Automatic Annotation of Tweets." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 2 (2020): 35–60. http://dx.doi.org/10.4018/ijcini.2020040103.

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Annotations are critical in various text mining tasks such as opinion mining, sentiment analysis, word sense disambiguation. Supervised learning algorithms start with the training of the classifier and require manually annotated datasets. However, manual annotations are often subjective, biased, onerous, and burdensome to develop; therefore, there is a need for automatic annotation. Automatic annotators automatically annotate the data for creating the training set for the supervised classifier, but lack subjectivity and ignore semantics of underlying textual structures. The objective of this r

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Grammens, Jonas, Annemieke Van Haver, Imelda Lumban-Gaol, Femke Danckaers, Peter Verdonk, and Jan Sijbers. "Automated Landmark Annotation for Morphometric Analysis of Distal Femur and Proximal Tibia." Journal of Imaging 10, no. 4 (2024): 90. http://dx.doi.org/10.3390/jimaging10040090.

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Manual anatomical landmarking for morphometric knee bone characterization in orthopedics is highly time-consuming and shows high operator variability. Therefore, automation could be a substantial improvement for diagnostics and personalized treatments relying on landmark-based methods. Applications include implant sizing and planning, meniscal allograft sizing, and morphological risk factor assessment. For twenty MRI-based 3D bone and cartilage models, anatomical landmarks were manually applied by three experts, and morphometric measurements for 3D characterization of the distal femur and prox

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Shadrova, Anna, Anke Lüdeling, Martin Klotz, Rahel Gajaneh Hartz, and Thomas Krause. "„Step away from the Computer!“." Zeitschrift für germanistische Linguistik 53, no. 1 (2025): 166–214. https://doi.org/10.1515/zgl-2025-2005.

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Abstract Linguistic research frequently requires the categorization of language phenomena in corpus data (annotation). Since those may occur plentifully, a partial or full automation of the annotation process appears attractive. The filtering and recombination of existing annotation layers seems to further provide an elegant solution to the deduction of higher-level annotations. In this contribution, we show at the example of German split particle verbs that this approach results in a number of linguistic, technological, and epistemological challenges related to the precise definition of the v

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Nurcahyawati, Vivine, and Zuriani Mustaffa. "Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation." Journal of Information Systems Engineering and Business Intelligence 9, no. 1 (2023): 108–18. http://dx.doi.org/10.20473/jisebi.9.1.108-118.

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Background: The concept of customer orientation, which is based on a set of fundamental beliefs that prioritize the interests of the customer, requires companies to detect these interests in order to maintain a high level of quality in their products or services. Furthermore, there are several indicators of customer orientation, and one of them is their opinion or taste, which provides valuable feedback for businesses. With the rapid development of social media, customers can express emotions, thoughts, and opinions about services or products that may not be easily conveyed in the real world.

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Del Rio, Iria, and Amália Mendes. "Error annotation in the COPLE2 corpus." Revista da Associação Portuguesa de Linguística, no. 4 (November 22, 2019): 225–39. http://dx.doi.org/10.26334//2183-9077/rapln4ano2018a42.

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We present the general architecture of the error annotation system applied to the COPLE2 corpus, a learner corpus of Portuguese implemented on the TEITOK platform. We give a general overview of the corpus and of the TEITOK functionalities and describe how the error annotation is structured in a two-level system: first, a fully manual token-based and coarse-grained annotation is applied and produces a rough classification of the errors in three categories, paired with multi-level information for POS and lemma; second, a multi-word and fine-grained annotation in standoff is then semi-automatical

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Del Rio, Iria, and Amália Mendes. "Error annotation in the COPLE2 corpus." Revista da Associação Portuguesa de Linguística, no. 4 (October 15, 2018): 225–39. http://dx.doi.org/10.26334/2183-9077/rapln4ano2018a42.

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We present the general architecture of the error annotation system applied to the COPLE2 corpus, a learner corpus of Portuguese implemented on the TEITOK platform. We give a general overview of the corpus and of the TEITOK functionalities and describe how the error annotation is structured in a two-level system: first, a fully manual token-based and coarse-grained annotation is applied and produces a rough classification of the errors in three categories, paired with multi-level information for POS and lemma; second, a multi-word and fine-grained annotation in standoff is then semi-automatical

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Fuoli, Matteo, and Charlotte Hommerberg. "Optimising transparency, reliability and replicability: annotation principles and inter-coder agreement in the quantification of evaluative expressions." Corpora 10, no. 3 (2015): 315–49. http://dx.doi.org/10.3366/cor.2015.0080.

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Manual corpus annotation facilitates exhaustive and detailed corpus-based analyses of evaluation that would not be possible with purely automatic techniques. However, manual annotation is a complex and subjective process. Most studies adopting this approach have paid insufficient attention to the methodological challenges involved in manually annotating evaluation – especially concerning transparency, reliability and replicability. This article illustrates a procedure for annotating evaluative expressions in text that facilitates more transparent, reliable and replicable analyses. The method i

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Lux, Mathias, Alexander Müller, and Mario Guggenberger. "Finding Image Regions with Human Computation and Games with a Purpose." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 8, no. 5 (2021): 41–43. http://dx.doi.org/10.1609/aiide.v8i5.12570.

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Manual image annotation is a tedious and time-consuming task, while automated methods are error prone and limited in their results. Human computation, and especially games with a purpose, have shown potential to create high quality annotations by "hiding the complexity" of the actual annotation task and employing the "wisdom of the crowds". In this demo paper we present two games with a single purpose: finding regions in images that correspond to given terms. We discuss approach, implementation, and preliminary results of our work and give an outlook to immediate future work.

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M, Sutharsan. "SMART ANALYSIS OF AUTOMATED AND SEMI-AUTOMATED APPROACHES TO DATA ANNOTATION FOR MACHINE LEARNING." ICTACT Journal on Data Science and Machine Learning 4, no. 3 (2023): 457–60. https://doi.org/10.21917/ijdsml.2023.0106.

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Data annotation for machine learning is the process of labeling data so that machines can properly identify patterns and other related information. It is a critical task within many artificial intelligence (AI) and machine learning (ML) projects. The traditional approach to data annotation involves manual input from a knowledgeable human expert. This, however, can be extremely costly, both in terms of time and money. To help reduce these costs, automated and semi-automated approaches to data annotation have been explored. Automated approaches are computer programs that label data automatically

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Zakharova, O. V. "Main Aspects of Big Data Semantic Annotation." PROBLEMS IN PROGRAMMING, no. 4 (December 2020): 022–33. http://dx.doi.org/10.15407/pp2020.04.022.

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Semantic annotations, due to their structure, are an integral part of the effective solution of big data problems. However, the problem of defining semantic annotations is not trivial. Manual annotation is not acceptable for big data due to their size and heterogeneity, as well as the complexity and cost of the annotation process, the automatic annotation task for big data has not yet decision. So, resolving the problem of semantic annotation requires modern mixed approaches, which would be based on and using the existing theoretical apparatus, namely methods and models of machine learning

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Gokul Prasad, M., T. Sumathi, and M. Hemalatha. "Semantic Web Image Search through Manual Annotation." International Journal of Computer Applications 17, no. 8 (2011): 39–42. http://dx.doi.org/10.5120/2238-2861.

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