Enhancing Event Candidate Acquisition for Event Linking

• URL: http://arxiv.org/abs/2609.13670v1
• Date: Sat, 12 Sep 2026 02:52:41 GMT
• Title: Enhancing Event Candidate Acquisition for Event Linking
• Abstract summary: Event linking associates event mentions in text with entries in a knowledge base (KB) or identifies them as out-of-KB events.<n>We present MACE, a Multi-Agent Candidate Event acquisition method that refines event structure before linking.
• Score: 23.877911978579508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event linking associates event mentions in text with entries in a knowledge base (KB), or identifies them as out-of-KB events. Although existing methods use different architectures, candidate event acquisition can still be weakened by short ambiguous mentions, noisy arguments, and evidence that is unevenly useful for retrieval. We present MACE, a Multi-Agent Candidate Event acquisition method that refines event structure before linking. MACE uses evidence-specialized LLM agents to acquire time, location, participant, and event-type evidence, exposes intermediate queries to candidate-event lookup tools, and lets a coordinator revise the evidence set before final candidate construction. Experiments on two event linking benchmarks show that adding MACE to different event linking models consistently improves accuracy. These results show that MACE improves event linking through better candidate event acquisition without modifying the event linking model.

Related papers

• Grounding Partially-Defined Events in Multimodal Data [61.0063273919745]
  We introduce a multimodal formulation for partially-defined events and cast the extraction of these events as a three-stage span retrieval task. We propose a benchmark for this task, MultiVENT-G, that consists of 14.5 hours of densely annotated current event videos and 1,168 text documents, containing 22.8K labeled event-centric entities. Results illustrate the challenges that abstract event understanding poses and demonstrates promise in event-centric video-language systems.
  arXiv  Detail & Related papers  (2024-10-07T17:59:48Z)
• DEGAP: Dual Event-Guided Adaptive Prefixes for Templated-Based Event Argument Extraction with Slot Querying [32.115904077731386]
  Recent advancements in event argument extraction (EAE) involve incorporating useful auxiliary information into models during training and inference.<n>These methods face two challenges: (1) the retrieval results may be irrelevant and (2) templates are developed independently for each event without considering their possible relationship.<n>We propose DEGAP to address these challenges through a simple yet effective components: dual prefixes, i.e. learnable prompt vectors, and an event-guided adaptive gating mechanism.
  arXiv  Detail & Related papers  (2024-05-22T03:56:55Z)
• Argument-Aware Approach To Event Linking [47.424863133787575]
  Event linking connects event mentions in text with relevant nodes in a knowledge base (KB) We improve event linking models by augmenting input text with tagged event argument information. We synthesize out-of-KB training examples from in-KB instances through controlled manipulation of event arguments.
  arXiv  Detail & Related papers  (2024-03-22T10:32:43Z)
• Prompt-based Graph Model for Joint Liberal Event Extraction and Event Schema Induction [1.3154296174423619]
  Events are essential components of speech and texts, describing the changes in the state of entities. The event extraction task aims to identify and classify events and find their participants according to event schemas. The researchers propose Liberal Event Extraction (LEE), which aims to extract events and discover event schemas simultaneously.
  arXiv  Detail & Related papers  (2024-03-19T07:56:42Z)
• Improving Event Definition Following For Zero-Shot Event Detection [66.27883872707523]
  Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types. We aim to improve zero-shot event detection by training models to better follow event definitions.
  arXiv  Detail & Related papers  (2024-03-05T01:46:50Z)
• CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities [16.888201607072318]
  Event coreference resolution (ECR) aims to group event mentions referring to the same real-world event into clusters. We propose a prompt-based approach, CorefPrompt, to transform ECR into a cloze-style (masked language model) task. This allows for simultaneous event modeling and coreference discrimination within a single template, with a fully shared context.
  arXiv  Detail & Related papers  (2023-10-23T02:47:27Z)
• Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning [45.3385722995475]
  We propose MetaEvent, a meta learning-based framework for zero- and few-shot event detection. In our framework, we propose to use the cloze-based prompt and a trigger-aware softr to efficiently project output to unseen event types. As such, the proposed MetaEvent can perform zero-shot event detection by mapping features to event types without any prior knowledge.
  arXiv  Detail & Related papers  (2023-05-27T05:36:46Z)
• Avoiding Post-Processing with Event-Based Detection in Biomedical Signals [69.34035527763916]
  We propose an event-based modeling framework that directly works with events as learning targets. We show that event-based modeling (without post-processing) performs on par with or better than epoch-based modeling with extensive post-processing.
  arXiv  Detail & Related papers  (2022-09-22T13:44:13Z)
• Unifying Event Detection and Captioning as Sequence Generation via Pre-Training [53.613265415703815]
  We propose a unified pre-training and fine-tuning framework to enhance the inter-task association between event detection and captioning. Our model outperforms the state-of-the-art methods, and can be further boosted when pre-trained on extra large-scale video-text data.
  arXiv  Detail & Related papers  (2022-07-18T14:18:13Z)
• PILED: An Identify-and-Localize Framework for Few-Shot Event Detection [79.66042333016478]
  In our study, we employ cloze prompts to elicit event-related knowledge from pretrained language models. We minimize the number of type-specific parameters, enabling our model to quickly adapt to event detection tasks for new types.
  arXiv  Detail & Related papers  (2022-02-15T18:01:39Z)
• Learning Constraints and Descriptive Segmentation for Subevent Detection [74.48201657623218]
  We propose an approach to learning and enforcing constraints that capture dependencies between subevent detection and EventSeg prediction. We adopt Rectifier Networks for constraint learning and then convert the learned constraints to a regularization term in the loss function of the neural model.
  arXiv  Detail & Related papers  (2021-09-13T20:50:37Z)
• Event-Driven Query Expansion [23.08079115356717]
  We propose a method to expand an event-related query by first detecting the events related to it. We derive the candidates for expansion as terms semantically related to both the query and the events. We show that our proposed method of leveraging events improves query expansion performance significantly compared with state-of-the-art methods on various newswire TREC datasets.
  arXiv  Detail & Related papers  (2020-12-22T14:56:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.