# SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble

arXiv: https://arxiv.org/abs/1910.08192 · HTML: https://mlsanity.com/paper/1910.08192/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2019.

## Built on by (most cited first, 25 of 25)

- [TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters](https://mlsanity.com/paper/2201.06771/) (2022)
- [Contrastive Learning with Hard Negative Entities for Entity Set Expansion](https://mlsanity.com/paper/2204.07789/) (2022)
- [Self-Supervised Euphemism Detection and Identification for Content Moderation](https://mlsanity.com/paper/2103.16808/) (2021)
- [Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples](https://mlsanity.com/paper/2402.07386/) (2024)
- [Automatic Context Pattern Generation for Entity Set Expansion](https://mlsanity.com/paper/2207.08087/) (2022)
- [Corpus-based Open-Domain Event Type Induction](https://mlsanity.com/paper/2109.03322/) (2021)
- [MESED: A Multi-modal Entity Set Expansion Dataset with Fine-grained Semantic Classes and Hard Negative Entities](https://mlsanity.com/paper/2307.14878/) (2023)
- [GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition](https://mlsanity.com/paper/2104.06230/) (2021)
- [A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion](https://mlsanity.com/paper/2402.13405/) (2024)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [TINKER: A framework for Open source Cyberthreat Intelligence](https://mlsanity.com/paper/2102.05571/) (2021)
- [Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision](https://mlsanity.com/paper/2203.14921/) (2022)
- [Schema-Guided Event Graph Completion](https://mlsanity.com/paper/2206.02921/) (2022)
- [CLIP also Understands Text: Prompting CLIP for Phrase Understanding](https://mlsanity.com/paper/2210.05836/) (2022)
- [From Retrieval to Generation: Efficient and Effective Entity Set Expansion](https://mlsanity.com/paper/2304.03531/) (2023)
- [Exploring the Implicit Semantic Ability of Multimodal Large Language Models: A Pilot Study on Entity Set Expansion](https://mlsanity.com/paper/2501.00330/) (2024)
- [Entity Set Co-Expansion in StackOverflow](https://mlsanity.com/paper/2212.02271/) (2022)
- [UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities](https://mlsanity.com/paper/2403.04247/) (2024)
- [Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model](https://mlsanity.com/paper/2204.07014/) (2022)
- [Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains](https://mlsanity.com/paper/2401.13129/) (2024)
- [CHisAgent: A Multi-Agent Framework for Event Taxonomy Construction in Ancient Chinese Cultural Systems](https://mlsanity.com/paper/2601.05520/) (2026)
- [GausSetExpander: A Simple Approach for Entity Set Expansion](https://mlsanity.com/paper/2202.13649/) (2022)
- [Advancing Relation Extraction through Language Probing with Exemplars from Set Co-Expansion](https://mlsanity.com/paper/2308.11720/) (2023)
- [Towards Visual Taxonomy Expansion](https://mlsanity.com/paper/2309.06105/) (2023)
- [BoostTaxo: Zero-Shot Taxonomy Induction via Boosting-Style Agentic Reasoning and Constraint-Aware Calibration](https://mlsanity.com/paper/2605.12520/) (2026)

## Abstract (arXiv)

Corpus-based set expansion (i.e., finding the "complete" set of entities belonging to the same semantic class, based on a given corpus and a tiny set of seeds) is a critical task in knowledge discovery. It may facilitate numerous downstream applications, such as information extraction, taxonomy induction, question answering, and web search. To discover new entities in an expanded set, previous approaches either make one-time entity ranking based on distributional similarity, or resort to iterative pattern-based bootstrapping. The core challenge for these methods is how to deal with noisy context features derived from free-text corpora, which may lead to entity intrusion and semantic drifting. In this study, we propose a novel framework, SetExpan, which tackles this problem, with two techniques: (1) a context feature selection method that selects clean context features for calculating entity-entity distributional similarity, and (2) a ranking-based unsupervised ensemble method for expanding entity set based on denoised context features. Experiments on three datasets show that SetExpan is robust and outperforms previous state-of-the-art methods in terms of mean average precision.