# Automatic Context Pattern Generation for Entity Set Expansion

arXiv: https://arxiv.org/abs/2207.08087 · HTML: https://mlsanity.com/paper/2207.08087/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 57 references that are in the map.
- First posted on arXiv 17 Jul 2022.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016)
- [Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models](https://mlsanity.com/paper/1610.02424/) (2016)
- [TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network](https://mlsanity.com/paper/2001.09522/) (2020)
- [Contrastive Learning with Hard Negative Entities for Entity Set Expansion](https://mlsanity.com/paper/2204.07789/) (2022)
- [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)
- [Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion](https://mlsanity.com/paper/2001.10106/) (2020)
- [Term Set Expansion based NLP Architect by Intel AI Lab](https://mlsanity.com/paper/1808.08953/) (2018)
- [SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery](https://mlsanity.com/paper/2009.13827/) (2020)
- [A Two-Stage Masked LM Method for Term Set Expansion](https://mlsanity.com/paper/2005.01063/) (2020)
- [A non-hierarchical attention network with modality dropout for textual response generation in multimodal dialogue systems](https://mlsanity.com/paper/2110.09702/) (2021)
- [Lightly-supervised Representation Learning with Global Interpretability](https://mlsanity.com/paper/1805.11545/) (2018)
- [Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion](https://mlsanity.com/paper/2109.12082/) (2021)
- [Entity Synonym Discovery via Multipiece Bilateral Context Matching](https://mlsanity.com/paper/1901.00056/) (2018)

## Built on by (most cited first, 21 of 21)

- [Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters](https://mlsanity.com/paper/2311.11268/) (2023)
- [Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment](https://mlsanity.com/paper/2302.08774/) (2023)
- [A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check](https://mlsanity.com/paper/2310.09119/) (2023)
- [MESED: A Multi-modal Entity Set Expansion Dataset with Fine-grained Semantic Classes and Hard Negative Entities](https://mlsanity.com/paper/2307.14878/) (2023)
- [Unleashing Potential of Evidence in Knowledge-Intensive Dialogue Generation](https://mlsanity.com/paper/2309.08380/) (2023)
- [A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion](https://mlsanity.com/paper/2402.13405/) (2024)
- [Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking](https://mlsanity.com/paper/2306.12245/) (2023)
- [Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework](https://mlsanity.com/paper/2402.11422/) (2024)
- [From Token to Line: Enhancing Code Generation with a Long-Term Perspective](https://mlsanity.com/paper/2504.07433/) (2025)
- [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)
- [MDIT: A Model-free Data Interpolation Method for Diverse Instruction Tuning](https://mlsanity.com/paper/2504.07288/) (2025)
- [Retrieval-Augmented Meta Learning for Low-Resource Text Classification](https://mlsanity.com/paper/2309.04979/) (2023)
- [MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correction](https://mlsanity.com/paper/2310.11671/) (2023)
- [DECIDER: A Dual-System Rule-Controllable Decoding Framework for Language Generation](https://mlsanity.com/paper/2403.01954/) (2024)
- [UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities](https://mlsanity.com/paper/2403.04247/) (2024)
- [HealthE: Classifying Entities in Online Textual Health Advice](https://mlsanity.com/paper/2210.03246/) (2022)
- [Investigating Graph Structure Information for Entity Alignment with Dangling Cases](https://mlsanity.com/paper/2304.04718/) (2023)
- [Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detection](https://mlsanity.com/paper/2309.04971/) (2023)
- [Corrections Meet Explanations: A Unified Framework for Explainable Grammatical Error Correction](https://mlsanity.com/paper/2502.15261/) (2025)
- [An Anchor Learning Approach for Citation Field Learning](https://mlsanity.com/paper/2309.03559/) (2023)

## Abstract (arXiv)

Entity Set Expansion (ESE) is a valuable task that aims to find entities of the target semantic class described by given seed entities. Various Natural Language Processing (NLP) and Information Retrieval (IR) downstream applications have benefited from ESE due to its ability to discover knowledge. Although existing corpus-based ESE methods have achieved great progress, they still rely on corpora with high-quality entity information annotated, because most of them need to obtain the context patterns through the position of the entity in a sentence. Therefore, the quality of the given corpora and their entity annotation has become the bottleneck that limits the performance of such methods. To overcome this dilemma and make the ESE models free from the dependence on entity annotation, our work aims to explore a new ESE paradigm, namely corpus-independent ESE. Specifically, we devise a context pattern generation module that utilizes autoregressive language models (e.g., GPT-2) to automatically generate high-quality context patterns for entities. In addition, we propose the GAPA, a novel ESE framework that leverages the aforementioned GenerAted PAtterns to expand target entities. Extensive experiments and detailed analyses on three widely used datasets demonstrate the effectiveness of our method. All the codes of our experiments are available at https://github.com/geekjuruo/GAPA.