# Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora

arXiv: https://arxiv.org/abs/1806.03191 · HTML: https://mlsanity.com/paper/1806.03191/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 26 of 26)

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- [End-to-End Ontology Learning with Large Language Models](https://mlsanity.com/paper/2410.23584/) (2024)
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- [Language Models for Lexical Inference in Context](https://mlsanity.com/paper/2102.05331/) (2021)
- [Conversational Negation using Worldly Context in Compositional Distributional Semantics](https://mlsanity.com/paper/2105.05748/) (2021)
- [CCGen: Explainable Complementary Concept Generation in E-Commerce](https://mlsanity.com/paper/2305.11480/) (2023)
- [Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision](https://mlsanity.com/paper/2203.14921/) (2022)
- [FLAME: Self-Supervised Low-Resource Taxonomy Expansion using Large Language Models](https://mlsanity.com/paper/2402.13623/) (2024)
- [Cross-lingual Word Embeddings in Hyperbolic Space](https://mlsanity.com/paper/2205.01907/) (2022)
- [Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules](https://mlsanity.com/paper/2308.16770/) (2023)
- [Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model](https://mlsanity.com/paper/2406.17739/) (2024)
- [On the Distinctive Co-occurrence Characteristics of Antonymy](https://mlsanity.com/paper/2509.11534/) (2025)
- [ASPER: Attention-based Approach to Extract Syntactic Patterns denoting Semantic Relations in Sentential Context](https://mlsanity.com/paper/2104.01523/) (2021)
- [UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation](https://mlsanity.com/paper/2210.03378/) (2022)
- [Probing Taxonomic and Thematic Embeddings for Taxonomic Information](https://mlsanity.com/paper/2301.10656/) (2023)
- [Learning Semantic Text Similarity to rank Hypernyms of Financial Terms](https://mlsanity.com/paper/2303.13475/) (2023)
- [Exploring Prompt-Based Methods for Zero-Shot Hypernym Prediction with Large Language Models](https://mlsanity.com/paper/2401.04515/) (2024)
- [Misalignment of Semantic Relation Knowledge between WordNet and Human Intuition](https://mlsanity.com/paper/2412.02138/) (2024)
- [Text-to-SQL Task-oriented Dialogue Ontology Construction](https://mlsanity.com/paper/2507.23358/) (2025)
- [A Benchmark for Text Expansion: Datasets, Metrics, and Baselines](https://mlsanity.com/paper/2309.09198/) (2023)
- [A Computational Analysis of Vagueness in Revisions of Instructional Texts](https://mlsanity.com/paper/2309.12107/) (2023)
- [RATIO: A Benchmark for Retrieval Across Typed Ideation Operations in Scientific Literature](https://mlsanity.com/paper/2608.27394/) (2026)
- [SPARROW: Scalable Taxonomy Induction via Structure-Preserving Partitioning and Constraint-Guided Merging](https://mlsanity.com/paper/2609.07307/) (2026)

## Abstract (arXiv)

Methods for unsupervised hypernym detection may broadly be categorized according to two paradigms: pattern-based and distributional methods. In this paper, we study the performance of both approaches on several hypernymy tasks and find that simple pattern-based methods consistently outperform distributional methods on common benchmark datasets. Our results show that pattern-based models provide important contextual constraints which are not yet captured in distributional methods.