EventPlus: A Temporal Event Understanding Pipeline
Mingyu Derek Ma, Jiao Sun, Mu Yang, Kung-Hsiang Huang, Nuan Wen,, Shikhar Singh, Rujun Han, Nanyun Peng

TL;DR
EventPlus is a comprehensive pipeline that integrates multiple state-of-the-art components to extract and understand temporal event information, aiding story comprehension and enabling domain adaptation.
Contribution
It introduces the first complete temporal event understanding pipeline that can be adapted across domains and is publicly available for broader use.
Findings
Provides detailed event annotations including temporal relations.
Demonstrates adaptability to biomedical domain.
Facilitates event-related information extraction.
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.
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Taxonomy
TopicsTopic Modeling · Biomedical Text Mining and Ontologies · Natural Language Processing Techniques
