# Unsupervised Label-aware Event Trigger and Argument Classification

arXiv: https://arxiv.org/abs/2012.15243 · HTML: https://mlsanity.com/paper/2012.15243/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2020.

## Built on by (most cited first, 27 of 27)

- [Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding](https://mlsanity.com/paper/2110.07476/) (2021)
- [GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles](https://mlsanity.com/paper/2205.12505/) (2022)
- [The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation](https://mlsanity.com/paper/2306.06918/) (2023)
- [Efficient Zero-shot Event Extraction with Context-Definition Alignment](https://mlsanity.com/paper/2211.05156/) (2022)
- [SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller](https://mlsanity.com/paper/2210.06694/) (2022)
- [The Art of Prompting: Event Detection based on Type Specific Prompts](https://mlsanity.com/paper/2204.07241/) (2022)
- [Zero-Shot On-the-Fly Event Schema Induction](https://mlsanity.com/paper/2210.06254/) (2022)
- [Global Constraints with Prompting for Zero-Shot Event Argument Classification](https://mlsanity.com/paper/2302.04459/) (2023)
- [Improving Event Definition Following For Zero-Shot Event Detection](https://mlsanity.com/paper/2403.02586/) (2024)
- [STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Models](https://mlsanity.com/paper/2305.15090/) (2023)
- [Few-shot Event Detection: An Empirical Study and a Unified View](https://mlsanity.com/paper/2305.01901/) (2023)
- [Targeted Augmentation for Low-Resource Event Extraction](https://mlsanity.com/paper/2405.08729/) (2024)
- [Identifying and Investigating Global News Coverage of Critical Events Such as Disasters and Terrorist Attacks](https://mlsanity.com/paper/2506.12925/) (2025)
- [Event Extraction: A Survey](https://mlsanity.com/paper/2210.03419/) (2022)
- [EDM3: Event Detection as Multi-task Text Generation](https://mlsanity.com/paper/2305.16357/) (2023)
- [On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction](https://mlsanity.com/paper/2402.18061/) (2024)
- [SNaRe: Domain-aware Data Generation for Low-Resource Event Detection](https://mlsanity.com/paper/2502.17394/) (2025)
- [A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction](https://mlsanity.com/paper/2305.15051/) (2023)
- [Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning](https://mlsanity.com/paper/2305.17373/) (2023)
- [RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts](https://mlsanity.com/paper/2406.06577/) (2024)
- [Learning to Generate and Extract: A Multi-Agent Collaboration Framework For Zero-shot Document-level Event Arguments Extraction](https://mlsanity.com/paper/2603.02909/) (2026)
- [P4E: Few-Shot Event Detection as Prompt-Guided Identification and Localization](https://mlsanity.com/paper/2202.07615/) (2022)
- [Are All Steps Equally Important? Benchmarking Essentiality Detection of Events](https://mlsanity.com/paper/2210.04074/) (2022)
- [Judicial Intelligent Assistant System: Extracting Events from Divorce Cases to Detect Disputes for the Judge](https://mlsanity.com/paper/2303.16751/) (2023)
- [Information Type Classification with Contrastive Task-Specialized Sentence Encoders](https://mlsanity.com/paper/2312.11020/) (2023)
- [Event prediction and causality inference despite incomplete information](https://mlsanity.com/paper/2406.05893/) (2024)
- [Task Decomposition for Efficient Annotation](https://mlsanity.com/paper/2606.24734/) (2026)

## Abstract (arXiv)

Identifying events and mapping them to pre-defined event types has long been an important natural language processing problem. Most previous work has been heavily relying on labor-intensive and domain-specific annotations while ignoring the semantic meaning contained in the labels of the event types. As a result, the learned models cannot effectively generalize to new domains, where new event types could be introduced. In this paper, we propose an unsupervised event extraction pipeline, which first identifies events with available tools (e.g., SRL) and then automatically maps them to pre-defined event types with our proposed unsupervised classification model. Rather than relying on annotated data, our model matches the semantics of identified events with those of event type labels. Specifically, we leverage pre-trained language models to contextually represent pre-defined types for both event triggers and arguments. After we map identified events to the target types via representation similarity, we use the event ontology (e.g., argument type "Victim" can only appear as the argument of event type "Attack") as global constraints to regularize the prediction. The proposed approach is shown to be very effective when tested on the ACE-2005 dataset, which has 33 trigger and 22 argument types. Without using any annotation, we successfully map 83% of the triggers and 54% of the arguments to the correct types, almost doubling the performance of previous zero-shot approaches.