Related papers: CEO: Corpus-based Open-Domain Event Ontology Induc…
Enterprise deep research often fails to produce decision-ready reports due to uneven information coverage, context explosion, and premature stopping. We propose a scalable Enterprise Deep Research (EDR) architecture to address these…
Cross-document event coreference resolution is a foundational task for NLP applications involving multi-text processing. However, existing corpora for this task are scarce and relatively small, while annotating only modest-size clusters of…
Complex Event Processing (CEP) is one technique used to the handling data flows. It allows pre-establishing conditions through rules and firing events when certain patterns are found in the data flows. Because the rules for defining such…
Most previous event extraction studies have relied heavily on features derived from annotated event mentions, thus cannot be applied to new event types without annotation effort. In this work, we take a fresh look at event extraction and…
Although the LLM-based in-context learning (ICL) paradigm has demonstrated considerable success across various natural language processing tasks, it encounters challenges in event detection. This is because LLMs lack an accurate…
Advances in Internet-of-Things (IoT) technologies have prompted the integration of IoT devices with business processes (BPs) in many organizations across various sectors, such as manufacturing, healthcare and smart spaces. The proliferation…
System logs are a critical resource for monitoring and managing distributed systems, providing insights into failures and anomalous behavior. Traditional log analysis techniques, including template-based and sequence-driven approaches,…
In many sensor network applications, a fusion center often has additional valuable information, such as context data, which cannot be obtained directly from the sensors. Motivated by this, we study a generalized CEO problem where a CEO has…
Overload situations, in the presence of resource limitations, in complex event processing (CEP) systems are typically handled using load shedding to maintain a given latency bound. However, load shedding might negatively impact the quality…
Enterprise Knowledge Graphs have become essential for unifying heterogeneous data and enforcing semantic governance. However, the construction of their underlying ontologies remains a resource-intensive, manual process that relies heavily…
Complex Event Recognition (CER for short) refers to the activity of detecting patterns in streams of continuously arriving data. This field has been traditionally approached from a practical point of view, resulting in heterogeneous…
This paper investigates models of event implications. Specifically, how well models predict entity state-changes, by targeting their understanding of physical attributes. Nominally, Large Language models (LLM) have been exposed to…
Recent works in Event Argument Extraction (EAE) have focused on improving model generalizability to cater to new events and domains. However, standard benchmarking datasets like ACE and ERE cover less than 40 event types and 25…
Objective Although social and environmental factors are central to provider patient interactions, the data that reflect these factors can be incomplete, vague, and subjective. We sought to create a conceptual framework to describe and…
Data-driven societal event forecasting methods exploit relevant historical information to predict future events. These methods rely on historical labeled data and cannot accurately predict events when data are limited or of poor quality.…
Stress, arising from the dynamic interaction between external stressors, individual appraisals, and physiological or psychological responses, significantly impacts health yet is often underreported and inconsistently documented, typically…
Incident response plays a pivotal role in mitigating the impact of cyber attacks. In recent years, the intensity and complexity of global cyber threats have grown significantly, making it increasingly challenging for traditional threat…
Document-level Event Causality Identification (DECI) aims to identify causal relations between event pairs in a document. It poses a great challenge of across-sentence reasoning without clear causal indicators. In this paper, we propose a…
It is a common practice in randomized clinical trials with the standard survival outcome to follow patients until a prespecified number of events have been observed, a type of trial known as the event-driven trial. The event-driven design…
In order to create a corpus exploration method providing topics that are easier to interpret than standard LDA topic models, here we propose combining two techniques called Entity linking and Labeled LDA. Our method identifies in an…