# Corpus-based Open-Domain Event Type Induction

arXiv: https://arxiv.org/abs/2109.03322 · HTML: https://mlsanity.com/paper/2109.03322/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 60 references that are in the map.
- First posted on arXiv 7 Sep 2021.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021)
- [Zero-Shot Transfer Learning for Event Extraction](https://mlsanity.com/paper/1707.01066/) (2017)
- [Weakly-Supervised Neural Text Classification](https://mlsanity.com/paper/1809.01478/) (2018)
- [Spherical Text Embedding](https://mlsanity.com/paper/1911.01196/) (2019)
- [Weakly-Supervised Hierarchical Text Classification](https://mlsanity.com/paper/1812.11270/) (2018)
- [SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble](https://mlsanity.com/paper/1910.08192/) (2019)
- [Empower Entity Set Expansion via Language Model Probing](https://mlsanity.com/paper/2004.13897/) (2020)
- [Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding](https://mlsanity.com/paper/2007.09536/) (2020)
- [Word Sense Induction with Neural biLM and Symmetric Patterns](https://mlsanity.com/paper/1808.08518/) (2018)
- [Open Domain Event Extraction Using Neural Latent Variable Models](https://mlsanity.com/paper/1906.06947/) (2019)
- [Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion](https://mlsanity.com/paper/2001.10106/) (2020)
- [Probabilistic Frame Induction](https://mlsanity.com/paper/1302.4813/) (2013)
- [Semi-Supervised Event Extraction with Paraphrase Clusters](https://mlsanity.com/paper/1808.08622/) (2018)
- [Open Event Extraction from Online Text using a Generative Adversarial Network](https://mlsanity.com/paper/1908.09246/) (2019)
- [SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery](https://mlsanity.com/paper/2009.13827/) (2020)
- [Joint Learning Templates and Slots for Event Schema Induction](https://mlsanity.com/paper/1603.01333/) (2016)
- [Extending Event Detection to New Types with Learning from Keywords](https://mlsanity.com/paper/1910.11368/) (2019)
- [Unsupervised Semantic Frame Induction using Triclustering](https://mlsanity.com/paper/1805.04715/) (2018)
- [Rapid Customization for Event Extraction](https://mlsanity.com/paper/1809.07783/) (2018)

## Built on by (most cited first, 18 of 18)

- [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021)
- [Unsupervised Slot Schema Induction for Task-oriented Dialog](https://mlsanity.com/paper/2205.04515/) (2022)
- [Open-Vocabulary Argument Role Prediction for Event Extraction](https://mlsanity.com/paper/2211.01577/) (2022)
- [Open Relation and Event Type Discovery with Type Abstraction](https://mlsanity.com/paper/2212.00178/) (2022)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [Unsupervised Key Event Detection from Massive Text Corpora](https://mlsanity.com/paper/2206.04153/) (2022)
- [Event Detection from Social Media for Epidemic Prediction](https://mlsanity.com/paper/2404.01679/) (2024)
- [Harvesting Event Schemas from Large Language Models](https://mlsanity.com/paper/2305.07280/) (2023)
- [An overview of event extraction and its applications](https://mlsanity.com/paper/2111.03212/) (2021)
- [Media Framing through the Lens of Event-Centric Narratives](https://mlsanity.com/paper/2410.03151/) (2024)
- [Semi-supervised New Event Type Induction and Description via Contrastive Loss-Enforced Batch Attention](https://mlsanity.com/paper/2202.05943/) (2022)
- [Beyond Grounding: Extracting Fine-Grained Event Hierarchies Across Modalities](https://mlsanity.com/paper/2206.07207/) (2022)
- [Open-Domain Event Graph Induction for Mitigating Framing Bias](https://mlsanity.com/paper/2305.12835/) (2023)
- [CEO: Corpus-based Open-Domain Event Ontology Induction](https://mlsanity.com/paper/2305.13521/) (2023)
- [Trigger-free Event Detection via Derangement Reading Comprehension](https://mlsanity.com/paper/2208.09659/) (2022)
- [A Structured Clustering Approach for Inducing Media Narratives](https://mlsanity.com/paper/2604.10368/) (2026)
- [Event Ontology Expansion via LLM-Based Conceptualization](https://mlsanity.com/paper/2606.21048/) (2026)
- [CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation](https://mlsanity.com/paper/2610.12580/) (2026)

## Abstract (arXiv)

Traditional event extraction methods require predefined event types and their corresponding annotations to learn event extractors. These prerequisites are often hard to be satisfied in real-world applications. This work presents a corpus-based open-domain event type induction method that automatically discovers a set of event types from a given corpus. As events of the same type could be expressed in multiple ways, we propose to represent each event type as a cluster of <predicate sense, object head> pairs. Specifically, our method (1) selects salient predicates and object heads, (2) disambiguates predicate senses using only a verb sense dictionary, and (3) obtains event types by jointly embedding and clustering <predicate sense, object head> pairs in a latent spherical space. Our experiments, on three datasets from different domains, show our method can discover salient and high-quality event types, according to both automatic and human evaluations.