EventPlus: A Temporal Event Understanding Pipeline
- URL: http://arxiv.org/abs/2101.04922v1
- Date: Wed, 13 Jan 2021 08:00:50 GMT
- Title: EventPlus: A Temporal Event Understanding Pipeline
- Authors: Mingyu Derek Ma, Jiao Sun, Mu Yang, Kung-Hsiang Huang, Nuan Wen,
Shikhar Singh, Rujun Han and Nanyun Peng
- Abstract summary: EventPlus is a temporal event understanding pipeline that integrates various state-of-the-art event understanding components.
We make EventPlus publicly available to facilitate event-related information extraction and downstream applications.
- Score: 12.313545429119651
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present EventPlus, a temporal event understanding pipeline that integrates
various state-of-the-art event understanding components including event trigger
and type detection, event argument detection, event duration and temporal
relation extraction. Event information, especially event temporal knowledge, is
a type of common sense knowledge that helps people understand how stories
evolve and provides predictive hints for future events. EventPlus as the first
comprehensive temporal event understanding pipeline provides a convenient tool
for users to quickly obtain annotations about events and their temporal
information for any user-provided document. Furthermore, we show EventPlus can
be easily adapted to other domains (e.g., biomedical domain). We make EventPlus
publicly available to facilitate event-related information extraction and
downstream applications.
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) - PromptCL: Improving Event Representation via Prompt Template and Contrastive Learning [3.481567499804089]
We present PromptCL, a novel framework for event representation learning.
PromptCL elicits the capabilities of PLMs to comprehensively capture the semantics of short event texts.
Our experimental results demonstrate that PromptCL outperforms state-of-the-art baselines on event related tasks.
arXiv Detail & Related papers (2024-04-27T12:22:43Z) - EVIT: Event-Oriented Instruction Tuning for Event Reasoning [18.012724531672813]
Event reasoning aims to infer events according to certain relations and predict future events.
Large language models (LLMs) have made significant advancements in event reasoning owing to their wealth of knowledge and reasoning capabilities.
However, smaller instruction-tuned models currently in use do not consistently demonstrate exceptional proficiency in managing these tasks.
arXiv Detail & Related papers (2024-04-18T08:14:53Z) - OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event
Understanding [53.23073872040206]
Event understanding aims at understanding the content and relationship of events within texts.
To facilitate related research and application, we present an event understanding toolkit OmniEvent.
arXiv Detail & Related papers (2023-09-25T16:15:09Z) - Rich Event Modeling for Script Event Prediction [60.67635412135682]
We propose the Rich Event Prediction (REP) framework for script event prediction.
REP contains an event extractor to extract such information from texts.
The core component of the predictor is a transformer-based event encoder to flexibly deal with an arbitrary number of arguments.
arXiv Detail & Related papers (2022-12-16T05:17:59Z) - 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) - The Art of Prompting: Event Detection based on Type Specific Prompts [28.878630198163556]
We develop a unified framework to incorporate the event type specific prompts for supervised, few-shot, and zero-shot event detection.
Our framework shows up to 24.3% F-score gain over the previous state-of-the-art baselines.
arXiv Detail & Related papers (2022-04-14T21:28:50Z) - Event Linking: Grounding Event Mentions to Wikipedia [63.087102209379864]
This work defines Event Linking, a new natural language understanding task at the event level.
Event linking tries to link an event mention, appearing in a news article for example, to the most appropriate Wikipedia page.
arXiv Detail & Related papers (2021-12-15T05:06:18Z) - Integrating Deep Event-Level and Script-Level Information for Script
Event Prediction [60.67635412135681]
We propose a Transformer-based model, called MCPredictor, which integrates deep event-level and script-level information for script event prediction.
The experimental results on the widely-used New York Times corpus demonstrate the effectiveness and superiority of the proposed model.
arXiv Detail & Related papers (2021-09-24T07:37:32Z)
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.