# CEO: Corpus-based Open-Domain Event Ontology Induction

arXiv: https://arxiv.org/abs/2305.13521 · HTML: https://mlsanity.com/paper/2305.13521/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 65 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://mlsanity.com/paper/1906.01749/) (2019)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021)
- [Multi-Sentence Argument Linking](https://mlsanity.com/paper/1911.03766/) (2019)
- [From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering](https://mlsanity.com/paper/2010.00402/) (2020)
- [A cost function for similarity-based hierarchical clustering](https://mlsanity.com/paper/1510.05043/) (2015)
- [Summarization as Indirect Supervision for Relation Extraction](https://mlsanity.com/paper/2205.09837/) (2022)
- [Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding](https://mlsanity.com/paper/2110.07476/) (2021)
- [Corpus-based Open-Domain Event Type Induction](https://mlsanity.com/paper/2109.03322/) (2021)
- [Automatic Event Salience Identification](https://mlsanity.com/paper/1809.00647/) (2018)
- [An Improved Cost Function for Hierarchical Cluster Trees](https://mlsanity.com/paper/1812.02715/) (2018)
- [Semi-supervised New Event Type Induction and Description via Contrastive Loss-Enforced Batch Attention](https://mlsanity.com/paper/2202.05943/) (2022)

## Built on by (most cited first, 3 of 3)

- [Event Ontology Expansion via LLM-Based Conceptualization](https://mlsanity.com/paper/2606.21048/) (2026)
- [Generative Ontology Induction: Domain-Agnostic Schema Discovery from Document Corpora Using Large Language Models](https://mlsanity.com/paper/2607.16201/) (2026)
- [CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation](https://mlsanity.com/paper/2610.12580/) (2026)

## Abstract (arXiv)

Existing event-centric NLP models often only apply to the pre-defined ontology, which significantly restricts their generalization capabilities. This paper presents CEO, a novel Corpus-based Event Ontology induction model to relax the restriction imposed by pre-defined event ontologies. Without direct supervision, CEO leverages distant supervision from available summary datasets to detect corpus-wise salient events and exploits external event knowledge to force events within a short distance to have close embeddings. Experiments on three popular event datasets show that the schema induced by CEO has better coverage and higher accuracy than previous methods. Moreover, CEO is the first event ontology induction model that can induce a hierarchical event ontology with meaningful names on eleven open-domain corpora, making the induced schema more trustworthy and easier to be further curated.