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Artigos de revistas sobre o tema "Model annotation"

Autor: Grafiati

Publicado: 31 de agosto de 2024

Última modificação: 19 de julho de 2025

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1

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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Liu, Zheng. "LDA-Based Automatic Image Annotation Model." Advanced Materials Research 108-111 (May 2010): 88–94. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.88.

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This paper presents LDA-based automatic image annotation by visual topic learning and related annotation extending. We introduce the Latent Dirichlet Allocation (LDA) model in visual application domain. Firstly, the visual topic which is most relevant to the unlabeled image is obtained. According to this visual topic, the annotations with highest likelihood serve as seed annotations. Next, seed annotations are extended by analyzing the relationship between seed annotations and related Flickr tags. Finally, we combine seed annotations and extended annotations to construct final annotation set.

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Okpala, Ebuka, and Long Cheng. "Large Language Model Annotation Bias in Hate Speech Detection." Proceedings of the International AAAI Conference on Web and Social Media 19 (June 7, 2025): 1389–418. https://doi.org/10.1609/icwsm.v19i1.35879.

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Large language models (LLMs) are fast becoming ubiquitous and have shown impressive performance in various natural language processing (NLP) tasks. Annotating data for downstream applications is a resource-intensive task in NLP. Recently, the use of LLMs as a cost-effective data annotator for annotating data used to train other models or as an assistive tool has been explored. Yet, little is known regarding the societal implications of using LLMs for data annotation. In this work, focusing on hate speech detection, we investigate how using LLMs such as GPT-4 and Llama-3 for hate speech detecti

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Paun, Silviu, Bob Carpenter, Jon Chamberlain, Dirk Hovy, Udo Kruschwitz, and Massimo Poesio. "Comparing Bayesian Models of Annotation." Transactions of the Association for Computational Linguistics 6 (December 2018): 571–85. http://dx.doi.org/10.1162/tacl_a_00040.

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The analysis of crowdsourced annotations in natural language processing is concerned with identifying (1) gold standard labels, (2) annotator accuracies and biases, and (3) item difficulties and error patterns. Traditionally, majority voting was used for 1, and coefficients of agreement for 2 and 3. Lately, model-based analysis of corpus annotations have proven better at all three tasks. But there has been relatively little work comparing them on the same datasets. This paper aims to fill this gap by analyzing six models of annotation, covering different approaches to annotator ability, item d

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Misirli, Goksel, Matteo Cavaliere, William Waites, et al. "Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization." Bioinformatics 32, no. 6 (2015): 908–17. http://dx.doi.org/10.1093/bioinformatics/btv660.

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Abstract Motivation: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling

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Li, Huadong, Ying Wei, Han Peng, and Wei Zhang. "DiffuPrompter: Pixel-Level Automatic Annotation for High-Resolution Remote Sensing Images with Foundation Models." Remote Sensing 16, no. 11 (2024): 2004. http://dx.doi.org/10.3390/rs16112004.

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Instance segmentation is pivotal in remote sensing image (RSI) analysis, aiding in many downstream tasks. However, annotating images with pixel-wise annotations is time-consuming and laborious. Despite some progress in automatic annotation, the performance of existing methods still needs improvement due to the high precision requirements for pixel-level annotation and the complexity of RSIs. With the support of large-scale data, some foundational models have made significant progress in semantic understanding and generalization capabilities. In this paper, we delve deep into the potential of t

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Chu, Zhendong, Jing Ma, and Hongning Wang. "Learning from Crowds by Modeling Common Confusions." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (2021): 5832–40. http://dx.doi.org/10.1609/aaai.v35i7.16730.

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Crowdsourcing provides a practical way to obtain large amounts of labeled data at a low cost. However, the annotation quality of annotators varies considerably, which imposes new challenges in learning a high-quality model from the crowdsourced annotations. In this work, we provide a new perspective to decompose annotation noise into common noise and individual noise and differentiate the source of confusion based on instance difficulty and annotator expertise on a per-instance-annotator basis. We realize this new crowdsourcing model by an end-to-end learning solution with two types of noise a

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Rotman, Guy, and Roi Reichart. "Multi-task Active Learning for Pre-trained Transformer-based Models." Transactions of the Association for Computational Linguistics 10 (2022): 1209–28. http://dx.doi.org/10.1162/tacl_a_00515.

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Abstract Multi-task learning, in which several tasks are jointly learned by a single model, allows NLP models to share information from multiple annotations and may facilitate better predictions when the tasks are inter-related. This technique, however, requires annotating the same text with multiple annotation schemes, which may be costly and laborious. Active learning (AL) has been demonstrated to optimize annotation processes by iteratively selecting unlabeled examples whose annotation is most valuable for the NLP model. Yet, multi-task active learning (MT-AL) has not been applied to state-

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Wen-Yi, Andrea W., Kathryn Adamson, Nathalie Greenfield, et al. "Automate or Assist? The Role of Computational Models in Identifying Gendered Discourse in US Capital Trial Transcripts." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7 (October 16, 2024): 1556–66. http://dx.doi.org/10.1609/aies.v7i1.31746.

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The language used by US courtroom actors in criminal trials has long been studied for biases. However, systematic studies for bias in high-stakes court trials have been difficult, due to the nuanced nature of bias and the legal expertise required. Large language models offer the possibility to automate annotation. But validating the computational approach requires both an understanding of how automated methods fit in existing annotation workflows and what they really offer. We present a case study of adding a computational model to a complex and high-stakes problem: identifying gender-biased l

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Luo, Yan, Tianxiu Lu, Weihan Zhang, Suiqun Li, and Xuefeng Wang. "Augmenting Three-Dimensional Model Annotation System with Enhanced Reality." Journal of Computing and Electronic Information Management 12, no. 2 (2024): 1–7. http://dx.doi.org/10.54097/uv15ws76.

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This study proposes an augmented reality-based three-dimensional model annotation system, integrating cloud anchors, three-dimensional reconstruction, and augmented reality technology to achieve explicit three-dimensional annotations on models. Employing an improved ORB algorithm, the annotated model is persistently anchored in three-dimensional space through cloud anchors, presenting accurate spatial information and showcasing the depth of scenes and relationships between elements. The system supports multiple data types for annotations, such as text and images. Through a comparison with trad

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Filali, Jalila, Hajer Baazaoui Zghal, and Jean Martinet. "Ontology-Based Image Classification and Annotation." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 11 (2020): 2040002. http://dx.doi.org/10.1142/s0218001420400029.

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With the rapid growth of image collections, image classification and annotation has been active areas of research with notable recent progress. Bag-of-Visual-Words (BoVW) model, which relies on building visual vocabulary, has been widely used in this area. Recently, attention has been shifted to the use of advanced architectures which are characterized by multi-level processing. Hierarchical Max-Pooling (HMAX) model has attracted a great deal of attention in image classification. To improve image classification and annotation, several approaches based on ontologies have been proposed. However,

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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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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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Pozharkova, I. N. "Context-Dependent Annotation Method in Emergency Monitoring Information Systems." Informacionnye Tehnologii 28, no. 1 (2022): 43–47. http://dx.doi.org/10.17587/it.28.43-47.

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The article presents the method of context-dependent annotations used in solving problems of emergency monitoring on the basis of information systems. The method is based on a spectral language model that allows solving various information search problems taking into account the specific features of the applied area. The functional model of emergency monitoring task in IDEF0 notation is presented. The task of context-dependent annotating operational summaries as a basis for generating preliminary reports is formulated. The main problems that arise in solving this problem on a large volume of i

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Bauer, Matthias, and Angelika Zirker. "Explanatory Annotation of Literary Texts and the Reader: Seven Types of Problems." International Journal of Humanities and Arts Computing 11, no. 2 (2017): 212–32. http://dx.doi.org/10.3366/ijhac.2017.0193.

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While most literary scholars wish to help readers understand literary texts by providing them with explanatory annotations, we want to go a step further and enable them, on the basis of structured information, to arrive at interpretations of their own. We therefore seek to establish a concept of explanatory annotation that is reader-oriented and combines hermeneutics with the opportunities provided by digital methods. In a first step, we are going to present a few examples of existing annotations that apparently do not take into account readerly needs. To us, they represent seven types of comm

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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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Hayat, Hassan, Carles Ventura, and Agata Lapedriza. "Modeling Subjective Affect Annotations with Multi-Task Learning." Sensors 22, no. 14 (2022): 5245. http://dx.doi.org/10.3390/s22145245.

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In supervised learning, the generalization capabilities of trained models are based on the available annotations. Usually, multiple annotators are asked to annotate the dataset samples and, then, the common practice is to aggregate the different annotations by computing average scores or majority voting, and train and test models on these aggregated annotations. However, this practice is not suitable for all types of problems, especially when the subjective information of each annotator matters for the task modeling. For example, emotions experienced while watching a video or evoked by other s

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Rao, Xun, Jiasheng Wang, Wenjing Ran, Mengzhu Sun, and Zhe Zhao. "Deep-Learning-Based Annotation Extraction Method for Chinese Scanned Maps." ISPRS International Journal of Geo-Information 12, no. 10 (2023): 422. http://dx.doi.org/10.3390/ijgi12100422.

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One of a map’s fundamental elements is its annotations, and extracting these annotations is an important step in enabling machine intelligence to understand scanned map data. Due to the complexity of the characters and lines, extracting annotations from scanned Chinese maps is difficult, and there is currently little research in this area. A deep-learning-based framework for extracting annotations from scanned Chinese maps is presented in the paper. Improved the EAST annotation detection model and CRNN annotation recognition model based on transfer learning make up the two primary parts of thi

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Ren, Jiaxin, Wanzeng Liu, Jun Chen, et al. "HI-CMAIM: Hybrid Intelligence-Based Multi-Source Unstructured Chinese Map Annotation Interpretation Model." Remote Sensing 17, no. 2 (2025): 204. https://doi.org/10.3390/rs17020204.

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Map annotation interpretation is crucial for geographic information extraction and intelligent map analysis. This study addresses the challenges associated with interpreting Chinese map annotations, specifically visual complexity and data scarcity issues, by proposing a hybrid intelligence-based multi-source unstructured Chinese map annotation interpretation method (HI-CMAIM). Firstly, leveraging expert knowledge in an innovative way, we constructed a high-quality expert knowledge-based map annotation dataset (EKMAD), which significantly enhanced data diversity and accuracy. Furthermore, an im

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Attik, Mohammed, Malik Missen, Mickaël Coustaty, et al. "OpinionML—Opinion Markup Language for Sentiment Representation." Symmetry 11, no. 4 (2019): 545. http://dx.doi.org/10.3390/sym11040545.

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It is the age of the social web, where people express themselves by giving their opinions about various issues, from their personal life to the world’s political issues. This process generates a lot of opinion data on the web that can be processed for valuable information, and therefore, semantic annotation of opinions becomes an important task. Unfortunately, existing opinion annotation schemes have failed to satisfy annotation challenges and cannot even adhere to the basic definition of opinion. Opinion holders, topical features and temporal expressions are major components of an opinion tha

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Li, Wei, Haiyu Song, Hongda Zhang, Houjie Li, and Pengjie Wang. "The Image Annotation Refinement in Embedding Feature Space based on Mutual Information." International Journal of Circuits, Systems and Signal Processing 16 (January 10, 2022): 191–201. http://dx.doi.org/10.46300/9106.2022.16.23.

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The ever-increasing size of images has made automatic image annotation one of the most important tasks in the fields of machine learning and computer vision. Despite continuous efforts in inventing new annotation algorithms and new models, results of the state-of-the-art image annotation methods are often unsatisfactory. In this paper, to further improve annotation refinement performance, a novel approach based on weighted mutual information to automatically refine the original annotations of images is proposed. Unlike the traditional refinement model using only visual feature, the proposed mo

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Cooling, Michael T., and Peter Hunter. "The CellML Metadata Framework 2.0 Specification." Journal of Integrative Bioinformatics 12, no. 2 (2015): 86–103. http://dx.doi.org/10.1515/jib-2015-260.

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Summary The CellML Metadata Framework 2.0 is a modular framework that describes how semantic annotations should be made about mathematical models encoded in the CellML (www.cellml.org) format, and their elements. In addition to the Core specification, there are several satellite specifications, each designed to cater for model annotation in a different context. Basic Model Information, Citation, License and Biological Annotation specifications are presented.

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Öhman, Emily, and Kaisla Kajava. "Sentimentator." Digital Humanities in the Nordic and Baltic Countries Publications 1, no. 1 (2018): 98–110. http://dx.doi.org/10.5617/dhnbpub.11013.

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We introduce Sentimentator; a publicly available gamified web-based annotation platform for fine-grained sentiment annotation at the sentence-level. Sentimentator is unique in that it moves beyond binary classification. We use a ten-dimensional model which allows for the annotation of 51 unique sentiments and emotions. The platform is gamified with a complex scoring system designed to reward users for high quality annotations. Sentimentator introduces several unique features that have previously not been available, or at best very limited, for sentiment annotation. In particular, it provides s

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Liu, Guojun, Yan Shi, Hongxu Huang, et al. "FPCAM: A Weighted Dictionary-Driven Model for Single-Cell Annotation in Pulmonary Fibrosis." Biology 14, no. 5 (2025): 479. https://doi.org/10.3390/biology14050479.

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The groundbreaking development of scRNA-seq has significantly improved cellular resolution. However, accurate cell-type annotation remains a major challenge. Existing annotation tools are often limited by their reliance on reference datasets, the heterogeneity of marker genes, and subjective biases introduced through manual intervention, all of which impact annotation accuracy and reliability. To address these limitations, we developed FPCAM, a fully automated pulmonary fibrosis cell-type annotation model. Built on the R Shiny platform, FPCAM utilizes a matrix of up-regulated marker genes and

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Tschöpe, Okka, Lutz Suhrbier, Anton Güntsch, and Walter Berendsohn. "AnnoSys – an online tool for sharing annotations to enhance data quality." Biodiversity Information Science and Standards 1 (August 15, 2017): e20315. https://doi.org/10.3897/tdwgproceedings.1.20315.

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AnnoSys is a web-based open-source information system that enables users to correct and enrich specimen data published in data portals, thus enhancing data quality and documenting research developments over time. This brings the traditional annotation workflows for specimens to the Internet, as annotations become visible to researchers who subsequently observe the annotated specimen. During its first phase, the AnnoSys project developed a fully functional prototype of an annotation data repository for complex and cross-linked XML-standardized data in the ABCD (Access to biological collection d

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Wang, Tian, Yuanye Ma, Catherine Blake, Masooda Bashir, and Hsin-Yuan Wang. "Taking disagreements into consideration: human annotation variability in privacy policy analysis." Information Research an international electronic journal 30, iConf (2025): 81–92. https://doi.org/10.47989/ir30iconf47581.

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Introduction. Privacy policies inform users about data practices but are often complex and difficult to interpret. Human annotation plays a key role in understanding privacy policies, yet annotation disagreements highlight the complexity of these texts. Traditional machine learning models prioritize consensus, overlooking annotation variability and its impact on accuracy. Method. This study examines how annotation disagreements affect machine learning performance using the OPP-115 corpus. It compares majority vote and union methods with alternative strategies to assess their impact on policy c

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Meunier, Loïc, Denis Baurain, and Luc Cornet. "AMAW: automated gene annotation for non-model eukaryotic genomes." F1000Research 12 (February 16, 2023): 186. http://dx.doi.org/10.12688/f1000research.129161.1.

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Background: The annotation of genomes is a crucial step regarding the analysis of new genomic data and resulting insights, and this especially for emerging organisms which allow researchers to access unexplored lineages, so as to expand our knowledge of poorly represented taxonomic groups. Complete pipelines for eukaryotic genome annotation have been proposed for more than a decade, but the issue is still challenging. One of the most widely used tools in the field is MAKER2, an annotation pipeline using experimental evidence (mRNA-seq and proteins) and combining different gene prediction tools

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Nichyporuk, Brennan, Jillian Cardinell, Justin Szeto, et al. "Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation." Machine Learning for Biomedical Imaging 1, December 2022 (2022): 1–37. http://dx.doi.org/10.59275/j.melba.2022-2d93.

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Generalization is an important attribute of machine learning models, particularly for those that are to be deployed in a medical context, where unreliable predictions can have real world consequences. While the failure of models to generalize across datasets is typically attributed to a mismatch in the data distributions, performance gaps are often a consequence of biases in the "ground-truth" label annotations. This is particularly important in the context of medical image segmentation of pathological structures (e.g. lesions), where the annotation process is much more subjective, and affecte

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Yeh, Eric, William Jarrold, and Joshua Jordan. "Leveraging Psycholinguistic Resources and Emotional Sequence Models for Suicide Note Emotion Annotation." Biomedical Informatics Insights 5s1 (January 2012): BII.S8979. http://dx.doi.org/10.4137/bii.s8979.

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We describe the submission entered by SRI International and UC Davis for the I2B2 NLP Challenge Track 2. Our system is based on a machine learning approach and employs a combination of lexical, syntactic, and psycholinguistic features. In addition, we model the sequence and locations of occurrence of emotions found in the notes. We discuss the effect of these features on the emotion annotation task, as well as the nature of the notes themselves. We also explore the use of bootstrapping to help account for what appeared to be annotator fatigue in the data. We conclude a discussion of future ave

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Li, Yongqi, Xin Miao, Mayi Xu, and Tieyun Qian. "Strong Empowered and Aligned Weak Mastered Annotation for Weak-to-Strong Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 26 (2025): 27437–45. https://doi.org/10.1609/aaai.v39i26.34955.

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The super-alignment problem of how humans can effectively supervise super-human AI has garnered increasing attention. Recent research has focused on investigating the weak-to-strong generalization (W2SG) scenario as an analogy for super-alignment. This scenario examines how a pre-trained strong model, supervised by an aligned weak model, can outperform its weak supervisor. Despite good progress, current W2SG methods face two main issues: 1) The annotation quality is limited by the knowledge scope of the weak model; 2) It is risky to position the strong model as the final corrector. To tackle t

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Mannai, Zayneb, Anis Kalboussi, and Ahmed Hadj Kacem. "Towards a Standard of Modelling Annotations in the E-Health Domain." Health Informatics - An International Journal 10, no. 04 (2021): 1–10. http://dx.doi.org/10.5121/hiij.2021.10401.

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A large number of annotation systems in e-health domain have been implemented in the literature. Several factors distinguish these systems from one another. In fact, each of these systems is based on a separate paradigm, resulting in a disorganized and unstructured vision. As part of our research, we attempted to categorize them based on the functionalities provided by each system, and we also proposed a model of annotations that integrates both the health professional and the patient in the process of annotating the medical file.

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Ma, Qin Yi, Li Hua Song, Da Peng Xie, and Mao Jun Zhou. "Development of CAD Model Annotation System Based on Design Intent." Applied Mechanics and Materials 863 (February 2017): 368–72. http://dx.doi.org/10.4028/www.scientific.net/amm.863.368.

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Most of the product design on the market is variant design or adaptive design, which need to reuse existing product design knowledge. A key aspect of reusing existing CAD model is correctly define and understand the design intents behind of existing CAD model, and this paper introduces a CAD model annotation system based on design intent. Design intents contained all design information of entire life cycle from modeling, analysis to manufacturing are marked onto the CAD model using PMI module in UG to improve the readability of the CAD model. Second, given the problems such as management diffi

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BALEY, Julien. "Leveraging graph algorithms to speed up the annotation of large rhymed corpora." Cahiers de Linguistique Asie Orientale 51, no. 1 (2022): 46–80. http://dx.doi.org/10.1163/19606028-bja10019.

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Abstract Rhyming patterns play a crucial role in the phonological reconstruction of earlier stages of Chinese. The past few years have seen the emergence of the use of graphs to model rhyming patterns, notably with List’s (2016) proposal to use graph community detection as a way to go beyond the limits of the link-and-bind method and test new hypotheses regarding phonological reconstruction. List’s approach requires the existence of a rhyme-annotated corpus; such corpora are rare and prohibitively expensive to produce. The present paper solves this problem by introducing several strategies to

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Xu, Yixuan, and Jingyi Cui. "Artificial Intelligence in Gene Annotation: Current Applications, Challenges, and Future Prospects." Theoretical and Natural Science 98, no. 1 (2025): 8–15. https://doi.org/10.54254/2753-8818/2025.21464.

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Gene annotation is a critical process in genomics that involves the description of not only the position but also the function of an encoded element of a genome. In general, this provides biological context to sequence data, enabling an advanced level of understanding of genetic information. This is important in areas aligned with genetic engineering, studies of diseases, and evolution. Through ML and DL methodologies, AI enhances functional annotation and gene prediction effectively and accurately. This review focuses on AI in genomic research and assesses its effectiveness compared to tradit

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Shang, Zirui, Yubo Zhu, Hongxi Li, Shuo Yang, and Xinxiao Wu. "Video Summarization Using Denoising Diffusion Probabilistic Model." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 7 (2025): 6776–84. https://doi.org/10.1609/aaai.v39i7.32727.

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Video summarization aims to eliminate visual redundancy while retaining key parts of video to construct concise and comprehensive synopses. Most existing methods use discriminative models to predict the importance scores of video frames. However, these methods are susceptible to annotation inconsistency caused by the inherent subjectivity of different annotators when annotating the same video. In this paper, we introduce a generative framework for video summarization that learns how to generate summaries from a probability distribution perspective, effectively reducing the interference of subj

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Yuan, Guowen, Ben Kao, and Tien-Hsuan Wu. "CEMA – Cost-Efficient Machine-Assisted Document Annotations." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 11043–50. http://dx.doi.org/10.1609/aaai.v37i9.26308.

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We study the problem of semantically annotating textual documents that are complex in the sense that the documents are long, feature rich, and domain specific. Due to their complexity, such annotation tasks require trained human workers, which are very expensive in both time and money. We propose CEMA, a method for deploying machine learning to assist humans in complex document annotation. CEMA estimates the human cost of annotating each document and selects the set of documents to be annotated that strike the best balance between model accuracy and human cost. We conduct experiments on comple

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Chanenson, Jake, Madison Pickering, and Noah Apthrope. "Automating Governing Knowledge Commons and Contextual Integrity (GKC-CI) Privacy Policy Annotations with Large Language Models." Proceedings on Privacy Enhancing Technologies 2025, no. 2 (2025): 280–308. https://doi.org/10.56553/popets-2025-0062.

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Identifying contextual integrity (CI) and governing knowledge commons (GKC) parameters in privacy policy texts can facilitate normative privacy analysis. However, GKC-CI annotation has heretofore required manual or crowdsourced effort. This paper demonstrates that high-accuracy GKC-CI parameter annotation of privacy policies can be performed automatically using large language models. We fine-tune 50 open-source and proprietary models on 21,588 ground truth GKC-CI annotations from 16 privacy policies. Our best performing model has an accuracy of 90.65%, which is comparable to the accuracy of ex

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Salek, Mahyar, Yoram Bachrach, and Peter Key. "Hotspotting — A Probabilistic Graphical Model For Image Object Localization Through Crowdsourcing." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 1156–62. http://dx.doi.org/10.1609/aaai.v27i1.8465.

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Object localization is an image annotation task which consists of finding the location of a target object in an image. It is common to crowdsource annotation tasks and aggregate responses to estimate the true annotation. While for other kinds of annotations consensus is simple and powerful, it cannot be applied to object localization as effectively due to the task's rich answer space and inherent noise in responses. We propose a probabilistic graphical model to localize objects in images based on responses from the crowd. We improve upon natural aggregation methods such as the mean and the med

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Wu, Aihua. "Ranking Biomedical Annotations with Annotator’s Semantic Relevancy." Computational and Mathematical Methods in Medicine 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/258929.

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Biomedical annotation is a common and affective artifact for researchers to discuss, show opinion, and share discoveries. It becomes increasing popular in many online research communities, and implies much useful information. Ranking biomedical annotations is a critical problem for data user to efficiently get information. As the annotator’s knowledge about the annotated entity normally determines quality of the annotations, we evaluate the knowledge, that is, semantic relationship between them, in two ways. The first is extracting relational information from credible websites by mining associ

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Zhang, Hansong, Shikun Li, Dan Zeng, Chenggang Yan, and Shiming Ge. "Coupled Confusion Correction: Learning from Crowds with Sparse Annotations." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16732–40. http://dx.doi.org/10.1609/aaai.v38i15.29613.

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As the size of the datasets getting larger, accurately annotating such datasets is becoming more impractical due to the expensiveness on both time and economy. Therefore, crowd-sourcing has been widely adopted to alleviate the cost of collecting labels, which also inevitably introduces label noise and eventually degrades the performance of the model. To learn from crowd-sourcing annotations, modeling the expertise of each annotator is a common but challenging paradigm, because the annotations collected by crowd-sourcing are usually highly-sparse. To alleviate this problem, we propose Coupled C

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Pimenov, I. S. "Analyzing Disagreements in Argumentation Annotation of Scientific Texts in Russian Language." NSU Vestnik. Series: Linguistics and Intercultural Communication 21, no. 2 (2023): 89–104. http://dx.doi.org/10.25205/1818-7935-2023-21-2-89-104.

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This paper presents the analysis of inter-annotator disagreements in modeling argumentation in scientific papers. The aim of the study is to specify annotation guidelines for the typical disagreement cases. The analysis focuses on inter-annotator disagreements at three annotation levels: theses identification, links construction between theses, specification of reasoning models for these links. The dataset contains 20 argumentation annotations for 10 scientific papers from two thematic areas, where two experts have independently annotated each text. These 20 annotations include 917 theses and

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Braylan, Alexander, Madalyn Marabella, Omar Alonso, and Matthew Lease. "A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation Tasks." Journal of Artificial Intelligence Research 78 (December 11, 2023): 901–73. http://dx.doi.org/10.1613/jair.1.14388.

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Human annotations are vital to supervised learning, yet annotators often disagree on the correct label, especially as annotation tasks increase in complexity. A common strategy to improve label quality is to ask multiple annotators to label the same item and then aggregate their labels. To date, many aggregation models have been proposed for simple categorical or numerical annotation tasks, but far less work has considered more complex annotation tasks, such as those involving open-ended, multivariate, or structured responses. Similarly, while a variety of bespoke models have been proposed for

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Bilal, Mühenad, Ranadheer Podishetti, Leonid Koval, Mahmoud A. Gaafar, Daniel Grossmann, and Markus Bregulla. "The Effect of Annotation Quality on Wear Semantic Segmentation by CNN." Sensors 24, no. 15 (2024): 4777. http://dx.doi.org/10.3390/s24154777.

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In this work, we investigate the impact of annotation quality and domain expertise on the performance of Convolutional Neural Networks (CNNs) for semantic segmentation of wear on titanium nitride (TiN) and titanium carbonitride (TiCN) coated end mills. Using an innovative measurement system and customized CNN architecture, we found that domain expertise significantly affects model performance. Annotator 1 achieved maximum mIoU scores of 0.8153 for abnormal wear and 0.7120 for normal wear on TiN datasets, whereas Annotator 3 with the lowest expertise achieved significantly lower scores. Sensiti

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Dijkema, Tom, and Sam Leeflang. "DiSSCover the Potential of FAIR Digital Object Annotations and How You Can Use Them!" Biodiversity Information Science and Standards 8 (August 7, 2024): e133172. https://doi.org/10.3897/biss.8.133172.

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The infrastructure for the Distributed System of Scientific Collections (DiSSCo) is in full development. Work within the DiSSCo Transition Project has been focused on building infrastructure, creating data models, and setting up Application Programming Interfaces (APIs) (Koureas et al. 2024). In the past years, DiSSCo has presented this work at different Biodiversity Information Standards (TDWG) conferences (Leeflang and Addink 2023, Leeflang et al. 2022, Addink et al. 2021). In this year's session, we would like to focus on the human-facing application: DiSSCover.DiSSCover is the graphical us

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Zhu, Zhen, Yibo Wang, Shouqing Yang, et al. "CORAL: Collaborative Automatic Labeling System Based on Large Language Models." Proceedings of the VLDB Endowment 17, no. 12 (2024): 4401–4. http://dx.doi.org/10.14778/3685800.3685885.

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In the era of big data, data annotation is integral to numerous applications. However, it is widely acknowledged as a laborious and time-consuming process, significantly impeding the scalability and efficiency of data-driven applications. To reduce the human cost, we demonstrate CORAL, a collaborative automatic labeling system driven by large language models (LLMs), which achieves high-quality annotation with the least human effort. Firstly, CORAL employs LLM to automatically annotate vast datasets, generating coarse-grained labels. Subsequently, a weakly-supervised learning module trains smal

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Spetale, Flavio E., Javier Murillo, Gabriela V. Villanova, Pilar Bulacio, and Elizabeth Tapia. "FGGA-lnc: automatic gene ontology annotation of lncRNA sequences based on secondary structures." Interface Focus 11, no. 4 (2021): 20200064. http://dx.doi.org/10.1098/rsfs.2020.0064.

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The study of long non-coding RNAs (lncRNAs), greater than 200 nucleotides, is central to understanding the development and progression of many complex diseases. Unlike proteins, the functionality of lncRNAs is only subtly encoded in their primary sequence. Current in-silico lncRNA annotation methods mostly rely on annotations inferred from interaction networks. But extensive experimental studies are required to build these networks. In this work, we present a graph-based machine learning method called FGGA-lnc for the automatic gene ontology (GO) annotation of lncRNAs across the three GO subdo

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Cornwell, Peter. "Progress with Repository-based Annotation Infrastructure for Biodiversity Applications." Biodiversity Information Science and Standards 7 (September 14, 2023): e112707. https://doi.org/10.3897/biss.7.112707.

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Rapid development since the 1980s of technologies for analysing texts, has led not only to widespread employment of text 'mining', but also to now-pervasive large language model artificial intelligence (AI) applications. However, building new, concise, data resources from historic, as well as contemporary scientific literature, which can be employed efficiently at scale by automation and which have long-term value for the research community, has proved more elusive.Efforts at codifying analyses, such as the Text Encoding Initiative (TEI), date from the early 1990s and were initially driven by

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Ramakrishnaiah, Yashpal, Adam P. Morris, Jasbir Dhaliwal, Melcy Philip, Levin Kuhlmann, and Sonika Tyagi. "Linc2function: A Comprehensive Pipeline and Webserver for Long Non-Coding RNA (lncRNA) Identification and Functional Predictions Using Deep Learning Approaches." Epigenomes 7, no. 3 (2023): 22. http://dx.doi.org/10.3390/epigenomes7030022.

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Long non-coding RNAs (lncRNAs), comprising a significant portion of the human transcriptome, serve as vital regulators of cellular processes and potential disease biomarkers. However, the function of most lncRNAs remains unknown, and furthermore, existing approaches have focused on gene-level investigation. Our work emphasizes the importance of transcript-level annotation to uncover the roles of specific transcript isoforms. We propose that understanding the mechanisms of lncRNA in pathological processes requires solving their structural motifs and interactomes. A complete lncRNA annotation fi

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Sakamoto, Nami, Takaki Oka, Yuki Matsuzawa, et al. "MS2Lipid: A Lipid Subclass Prediction Program Using Machine Learning and Curated Tandem Mass Spectral Data." Metabolites 14, no. 11 (2024): 602. http://dx.doi.org/10.3390/metabo14110602.

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Background: Untargeted lipidomics using collision-induced dissociation-based tandem mass spectrometry (CID-MS/MS) is essential for biological and clinical applications. However, annotation confidence still relies on manual curation by analytical chemists, despite the development of various software tools for automatic spectral processing based on rule-based fragment annotations. Methods: In this study, we present a novel machine learning model, MS2Lipid, for the prediction of known lipid subclasses from MS/MS queries, providing an orthogonal approach to existing lipidomics software programs in

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Julian, Sahertian, and Akbar Saiful. "Automatic Image Annotation Using CMRM with Scene Information." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 2 (2017): 693–701. https://doi.org/10.12928/TELKOMNIKA.v15i2.5160.

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Searching of digital images in a disorganized image collection is a challenging problem. One step of image searching is automatic image annotation. Automatic image annotation refers to the process of automatically assigning relevant text keywords to any given image, reflecting its content. In the past decade many automatic image annotation methods have been proposed and achieved promising result. However, annotation prediction from the methods is still far from accurate. To tackle this problem, in this paper we propose an automatic annotation method using relevance model and scene information.

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