# PatternRank: Leveraging Pretrained Language Models and Part of Speech for Unsupervised Keyphrase Extraction

arXiv: https://arxiv.org/abs/2210.05245 · HTML: https://mlsanity.com/paper/2210.05245/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features](https://mlsanity.com/paper/1703.02507/) (2017)
- [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017)
- [Deep Keyphrase Generation](https://mlsanity.com/paper/1704.06879/) (2017)
- [Simple Unsupervised Keyphrase Extraction using Sentence Embeddings](https://mlsanity.com/paper/1801.04470/) (2018)
- [A Review of Keyphrase Extraction](https://mlsanity.com/paper/1905.05044/) (2019)

## Built on by (most cited first, 19 of 19)

- [Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches](https://mlsanity.com/paper/2211.16285/) (2022)
- [A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression](https://mlsanity.com/paper/2311.13852/) (2023)
- [Exploring the Landscape of Natural Language Processing Research](https://mlsanity.com/paper/2307.10652/) (2023)
- [Efficient Domain Adaptation of Sentence Embeddings Using Adapters](https://mlsanity.com/paper/2307.03104/) (2023)
- [AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge](https://mlsanity.com/paper/2307.07851/) (2023)
- [GraphiT: Efficient Node Classification on Text-Attributed Graphs with Prompt Optimized LLMs](https://mlsanity.com/paper/2502.10522/) (2025)
- [Theme-driven Keyphrase Extraction to Analyze Social Media Discourse](https://mlsanity.com/paper/2301.11508/) (2023)
- [Automated Question Generation for Science Tests in Arabic Language Using NLP Techniques](https://mlsanity.com/paper/2406.08520/) (2024)
- [Pre-Trained Language Models for Keyphrase Prediction: A Review](https://mlsanity.com/paper/2409.01087/) (2024)
- [EntropyRank: Unsupervised Keyphrase Extraction via Side-Information Optimization for Language Model-based Text Compression](https://mlsanity.com/paper/2308.13399/) (2023)
- [An Improved Method for Class-specific Keyword Extraction: A Case Study in the German Business Registry](https://mlsanity.com/paper/2407.14085/) (2024)
- [LongKey: Keyphrase Extraction for Long Documents](https://mlsanity.com/paper/2411.17863/) (2024)
- [Applying unsupervised keyphrase methods on concepts extracted from discharge sheets](https://mlsanity.com/paper/2303.08928/) (2023)
- [Unsupervised extraction of local and global keywords from a single text](https://mlsanity.com/paper/2307.14005/) (2023)
- [Brand Network Booster: A new system for improving brand connectivity](https://mlsanity.com/paper/2309.16228/) (2023)
- [Enterprise Use Cases Combining Knowledge Graphs and Natural Language Processing](https://mlsanity.com/paper/2404.01443/) (2024)
- [Dynamik: Syntactically-Driven Dynamic Font Sizing for Emphasis of Key Information](https://mlsanity.com/paper/2504.09734/) (2025)
- [SPAR: Session-based Pipeline for Adaptive Retrieval on Legacy File Systems](https://mlsanity.com/paper/2512.12938/) (2025)
- [Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings](https://mlsanity.com/paper/2606.10716/) (2026)

## Abstract (arXiv)

Keyphrase extraction is the process of automatically selecting a small set of most relevant phrases from a given text. Supervised keyphrase extraction approaches need large amounts of labeled training data and perform poorly outside the domain of the training data. In this paper, we present PatternRank, which leverages pretrained language models and part-of-speech for unsupervised keyphrase extraction from single documents. Our experiments show PatternRank achieves higher precision, recall and F1-scores than previous state-of-the-art approaches. In addition, we present the KeyphraseVectorizers package, which allows easy modification of part-of-speech patterns for candidate keyphrase selection, and hence adaptation of our approach to any domain.