---
title: "Lexicalised and non-lexicalized multi-word — Maziarz 2023"
description: "Princeton WordNet contains many multi-word entries, but not all of them are genuine lexical units."
authors: ["Marek Maziarz", "Łukasz Grabowski", "Tadeusz Piotrowski", "Ewa Rudnicka", "Maciej Piasecki"]
year: 2023
venue: "Proceedings of the 12th Global Wordnet Conference"
source: "https://aclanthology.org/2023.gwc-1.28/"
doi: "10.18653/v1/2023.gwc-1.28"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/maziarz-2023-lexicalised-non-lexicalized-multi-word/"
---
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# Lexicalised and non-lexicalized multi-word expressions in WordNet: a cross-encoder approach

**Authors:** Marek Maziarz, Łukasz Grabowski, Tadeusz Piotrowski, Ewa Rudnicka, Maciej Piasecki · **Year:** 2023 · **Venue:** Proceedings of the 12th Global Wordnet Conference · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.gwc-1.28/) · [PDF](https://aclanthology.org/2023.gwc-1.28.pdf) · [DOI 10.18653/v1/2023.gwc-1.28](https://doi.org/10.18653/v1/2023.gwc-1.28) · [OpenAlex](https://openalex.org/W7103896650)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
Princeton WordNet contains many multi-word entries, but not all of them are genuine lexical units. Some are taxonomy labels, quantifier phrases or ordinary collocations. The authors want to separate lexicalised multi-word lexical units (MWLUs) from ad hoc combinations, and they improve on their earlier rule-based and logistic-regression approach.

They collected about 39k multi-word entries from Princeton WordNet and enWordNet, excluding proper names and chemical and biological taxonomy terms. A sample of 387 items was labelled by checking whether six major English dictionaries list each one as a headword. They then fine-tuned a cross-encoder (setu4993/smaller-LaBSE, via sentence-transformers) on pairs of a lemma and its gloss, with the gloss prefixed by hypernym lemmas.

Under .632 bootstrap validation, the model reached roughly 0.78 F1 on the MWLU class, a clear gain over the earlier feature-based model, though it was not significantly better than the majority-class baseline on that measure.

The work offers a way to clean WordNet's MWE inventory, which NLP research often uses as a gold standard. Because the model is language-agnostic, it could be transferred to other wordnets, such as Polish.

## Key points
- **Dataset and labels:** 39,406 MWEs were drawn from Princeton WordNet and enWordNet, and 86% of them are nouns. The annotated sample of 387 items contains 243 MWLUs and 144 non-MWLUs, labelled against NODE, M-W, Collins, New World, COBUILD and Longman.
- **Model setup:** the model input is the lemma paired with hypernym lemmas plus the definition, truncated to 48 tokens. Training ran for 100 bootstrap iterations of 4 epochs each.
- **Results:** for the MWLU class, precision, recall and F1 were each about 0.78; for non-MWLU they were about 0.62–0.63.
- **Comparisons:**
  - MWLU F1 rose from 58% with the earlier logistic regression to 78% (p\<0.01), while non-MWLU F1 was essentially unchanged.
  - Against the majority baseline (F1 0.77), the MWLU F1 difference was not significant (p=.32).
- **Application and release:** applied to the full WordNet set, the model tagged 25.5k entries as lexicalised. The data are released on GitHub (MarekMaziarz/MWE-recognition-in-WN) under CC BY 4.0.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{maziarz2023lexicalised,
  title = {{Lexicalised and non-lexicalized multi-word expressions in WordNet: a cross-encoder approach}},
  author = {Maziarz, Marek and Grabowski, Łukasz and Piotrowski, Tadeusz and Rudnicka, Ewa and Piasecki, Maciej},
  booktitle = {Proceedings of the 12th Global Wordnet Conference},
  year = {2023},
  doi = {10.18653/v1/2023.gwc-1.28},
  url = {https://doi.org/10.18653/v1/2023.gwc-1.28}
}
```

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