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Lexicalised and non-lexicalized multi-word expressions in WordNet: a cross-encoder approach

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

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.gwc-1.28

@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}
}

References

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