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•Events represent experiences or processes that unfold in time, often having distinct phases.•Event markers are identified time points usually associated with a phase transition of an event.•The critical linkage of experimental data to external reality and processes is achieved by creating appropriate event markers and associating these markers with informative metadata.•The HED (Hierarchical event Descriptor) system provides a framework and tools for making this association in a machine-actionable, analysis-ready way.•Without appropriate event design (appropriate event markers and informative annotation) neuroimaging data will not be usable to its full potential by the broader community. Event-related data analysis plays a central role in EEG and MEG (MEEG) and other neuroimaging modalities including fMRI. Choices about which events to report and how to annotate their full natures significantly influence the value, reliability, and reproducibility of neuroimaging datasets for further analysis and meta- or mega-analysis. A powerful annotation strategy using the new third-generation formulation of the Hierarchical Event Descriptors (HED) framework and tools (hedtags.org) combines robust event description with details of experiment design and metadata in a human-readable as well as machine-actionable form, making event annotation relevant to the full range of neuroimaging and other time series data. This paper considers the event design and annotation process using as a case study the well-known multi-subject, multimodal dataset of Wakeman and Henson made available by its authors as a Brain Imaging Data Structure (BIDS) dataset (bids.neuroimaging.io). We propose a set of best practices and guidelines for event annotation integrated in a natural way into the BIDS metadata file architecture, examine the impact of event design decisions, and provide a working example of organizing events in MEEG and other neuroimaging data. We demonstrate how annotations using HED can document events occurring during neuroimaging experiments as well as their interrelationships, providing machine-actionable annotation enabling automated within- and across-experiment analysis and comparisons. We discuss the evolution of HED software tools and have made available an accompanying HED-annotated BIDS-formated edition of the MEEG data of the Wakeman and Henson dataset (openneuro.org, ds003645). [Display omitted]

Event extraction(EE) is a challenging task of information extraction, which aims to extract structured event information from text. Existing methods usually achieve state-of-the-art performance based on pre-trained language models(PLMs) that exploit the semantic information of triggers and arguments. However, these methods struggle in extraction due to their lack of two intrinsic considerations: (a) the complexity of event structure; (b) the impact of exact event type on event extraction. In this paper, we propose an event annotation enhanced BERT framework, termed EABERT. Specifically,event annotations are predefined and can be considered as a complete event structure. We inject additional event knowledge into the model by incorporating event annotations into the model input. Furthermore, to incorporate appropriate event annotations into the model, we employ the bilateral-branch BERT network to train the event type classifier for better accuracy of event annotations. Experiments on the event extraction benchmark dataset (ACE 2005, MAVEN) significantly improved our proposed framework compared to previous approaches.

News organizations increasingly tailor their news offering to the reader through personalized recommendation algorithms. However, automated recommendation algorithms reflect a commercial logic based on calculated relevance to the user, rather than aiming at a well-informed citizenry. In this paper, we introduce the EventDNA corpus, a dataset of 1773 Dutch-language news articles annotated with information on entities, news events and IPTC Media Topic codes, with the ultimate goal to outline a recommendation algorithm that uses news event diversity rather than previous reading behaviour as a key driver for personalized news recommendation. We describe the EventDNA annotation guidelines, which are inspired by the well-known ERE framework and conclude that it is not practical to apply a fixed event typology such as used in ERE to an unrestricted data context. The corpus and related source code is made available at https://github.com/NewsDNA-LT3/.github.

Human electrophysiological and related time series data are often acquired in complex, event-rich environments. However, the resulting recorded brain or other dynamics are often interpreted in relation to more sparsely recorded or subsequently-noted events. Currently a substantial gap exists between the level of event description required by current digital data archiving standards and the level of annotation required for successful analysis of event-related data across studies, environments, and laboratories. Manifold challenges must be addressed, most prominently ontological clarity, vocabulary extensibility, annotation tool availability, and overall usability, to allow and promote sharing of data with an effective level of descriptive detail for labeled events. Motivating data authors to perform the work needed to adequately annotate their data is a key challenge. This paper describes new developments in the Hierarchical Event Descriptor (HED) system for addressing these issues. We recap the evolution of HED and its acceptance by the Brain Imaging Data Structure (BIDS) movement, describe the recent release of HED-3G, a third generation HED tools and design framework, and discuss directions for future development. Given consistent, sufficiently detailed, tool-enabled, field-relevant annotation of the nature of recorded events, prospects are bright for large-scale analysis and modeling of aggregated time series data, both in behavioral and brain imaging sciences and beyond.

Due to the large number of photos that are currently being generated, it is very important to have techniques to organize, search for, and retrieve such images. Photo annotation plays a key role in these mechanisms because it can link raw data (photos) to specific information that is essential for human beings to handle large amounts of content. However, the generation of photo annotation is still a difficult problem to solve as part of a well-known challenge called the semantic gap. In this paper, a literature review was conducted with the aim of investigating the most popular methods employed to produce photo annotations. Based on the papers surveyed, we identified that People (“Who?”), Location (“Where?”), and Event (“Where? When?”) are the most important features of photo annotation. We also established comparisons between similar photo annotation methods, highlighting key aspects of the most commonly used approaches. Moreover, we provide an overview of a general photo annotation process and present the main aspects of photo annotation representation comprising formats, context of usage, advantages and disadvantages. Finally, we discuss ways to improve photo annotation methods and present some future research guidelines.

Event coreference resolution is a task in which different text fragments that refer to the same real-world event are automatically linked together. This task can be performed not only within a single document but also across different documents and can serve as a basis for many useful Natural Language Processing applications. Resources for this type of research, however, are extremely limited. We compiled the first large-scale dataset for cross-document event coreference resolution in Dutch, comparable in size to the most widely used English event coreference corpora. As data for event coreference is notoriously sparse, we took additional steps to maximize the number of coreference links in our corpus. Due to the complex nature of event coreference resolution, many algorithms consist of pipeline architectures which rely on a series of upstream tasks such as event detection, event argument identification and argument coreference. We tackle the task of event argument coreference to both illustrate the potential of our compiled corpus and to lay the groundwork for a Dutch event coreference resolution system in the future. Results show that existing NLP algorithms can be easily retrofitted to contribute to the subtasks of an event coreference resolution pipeline system.

This article proposes an automatic and semi-automatic annotation technique for people in photos using the shared event concept, which consists of many photos captured by different devices of people who attended the same event. The technique uses an algorithm to group photos into personal events and then verifies which of these events are shared. The automatic annotation of people uses techniques of facial recognition and detection, while the semi-automatic annotation uses a pondered sum of estimators based on contextual information and picture content. Experiments showed that using the shared event concept increases the hit rate of automatic and semi-automatic annotations of people in the utilized photo collection.

In the last decade, factuality has undeniably been an area of growing interest in Natural Language Processing. This paper describes a rule-based tool to automatically identify the factual status of events in Spanish text, understood with respect to the degree of commitment with which a narrator presents situations. Factuality is represented compositionally, considering the following semantic categories: commitment, polarity, event structure, and time. In contrast with neural machine learning approaches, this tool is entirely based on manually created lexico-syntactic rules that systematize semantic and syntactic patterns of factuality. Thus, it is able to provide explanations for automatic decisions, which are very valuable to guarantee accountability of the system. We evaluate the performance of the system by comparison with a manually annotated Gold Standard, obtaining results that are comparable, if not better, to machine learning approaches for a related task, the FACT 2019 challenge at the IBERLEF evaluation forum.

We present SENTiVENT, a corpus of fine-grained company-specific events in English economic news articles. The domain of event processing is highly productive and various general domain, fine-grained event extraction corpora are freely available but economically-focused resources are lacking. This work fills a large need for a manually annotated dataset for economic and financial text mining applications. A representative corpus of business news is crawled and an annotation scheme developed with an iteratively refined economic event typology. The annotations are compatible with benchmark datasets (ACE/ERE) so state-of-the-art event extraction systems can be readily applied. This results in a gold-standard dataset annotated with event triggers, participant arguments, event co-reference, and event attributes such as type, subtype, negation, and modality. An adjudicated reference test set is created for use in annotator and system evaluation. Agreement scores are substantial and annotator performance adequate, indicating that the annotation scheme produces consistent event annotations of high quality. In an event detection pilot study, satisfactory results were obtained with a macro-averaged F₁-score of 59% validating the dataset for machine learning purposes. This dataset thus provides a rich resource on events as training data for supervised machine learning for economic and financial applications. The dataset and related source code is made available at https://osf.io/8jec2/.

This study explores the potential of using large language models to assist content analysis by conducting a case study to identify adverse events (AEs) in social media posts. The case study compares ChatGPT’s performance with human annotators’ in detecting AEs associated with delta-8-tetrahydrocannabinol, a cannabis-derived product. Using the identical instructions given to human annotators, ChatGPT closely approximated human results, with a high degree of agreement noted: 94.4% (9436/10,000) for any AE detection (Fleiss κ=0.95) and 99.3% (9931/10,000) for serious AEs (κ=0.96). These findings suggest that ChatGPT has the potential to replicate human annotation accurately and efficiently. The study recognizes possible limitations, including concerns about the generalizability due to ChatGPT’s training data, and prompts further research with different models, data sources, and content analysis tasks. The study highlights the promise of large language models for enhancing the efficiency of biomedical research.