PatternRank - Leveraging Pretrained Language Models and Part of Speech for Unsupervised Keyphrase Extraction
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.
*https://github.com/TimSchopf/KeyphraseVectorizers
Blog articles about this paper:
- Unsupervised Keyphrase Extraction with PatternRank
- Keyphrase Extraction with BERT Transformers and Noun Phrases
| Attribute | Value |
|---|---|
| Address | Valletta, Malta |
| Authors | Tim Schopf , Simon KIimеk , Prof. Dr. Florian Matthes |
| Citation | @conference{schopf_etal_kdir22, author={Tim Schopf and Simon Klimek and Florian Matthes}, title={PatternRank: Leveraging Pretrained Language Models and Part of Speech for Unsupervised Keyphrase Extraction}, booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR}, year={2022}, pages={243-248}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0011546600003335}, isbn={978-989-758-614-9}, issn={2184-3228}, } |
| Key | Sc22c |
| Research project | Research Institution Knowledge Graph (RIKG) |
| Title | PatternRank: Leveraging Pretrained Language Models and Part of Speech for Unsupervised Keyphrase Extraction |
| Type of publication | Conference |
| Year | 2022 |
| Publication URL | https://www.scitepress.org/Link.aspx?doi=10.5220/0011546600003335 |
| Project | Research Institution Knowledge Graph (RIKG) |
| Acronym | |
| Team members |