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Automatically Constructing a Lexicon of Verb Phrase Idiomatic Combinations

Summary

The paper tackles verb+noun idiomatic combinations (VNICs) in English, such as shoot the breeze or pull strings. These phrases look like ordinary verb–object phrases but carry non-literal meanings, and they vary in how much they can be changed. NLP lexicons and systems need to tell them apart from literal phrases and to know which variant forms each idiom allows.

Linguistic theory holds that more idiomatic expressions are less open to word substitution and syntactic change. Building on that idea, the authors design corpus-based statistical measures of lexical, syntactic and combined fixedness for verb–noun pairs.

  • Lexical fixedness compares the PMI of the target pair with the PMI of variants made by swapping the noun for similar words from Lin's thesaurus.
  • Syntactic fixedness looks at how a pair is spread across patterns involving passivization, determiner type and pluralization.

These measures separate idiomatic from literal combinations clearly better than a PMI collocation baseline. The authors also propose a z-score method that automatically finds an idiom's canonical forms (Cforms) for inclusion in a lexicon. This reduces the need for hand annotation when building idiom lexicons.

Key points

  • Lexical fixedness: measured by how far the target pair's PMI deviates from the mean PMI of its noun-substituted variants, normalized by the standard deviation. Only the noun is varied, because verb substitutions often produce other idioms (e.g., keep/lose one's cool). Similar nouns come from the automatically built thesaurus of Lin (1998).
  • Syntactic fixedness: based on three kinds of variation that literal phrases tolerate but many VNICs resist:
    • passivization,
    • determiner choice (none, the, demonstrative, possessive, other),
    • singular vs. plural noun.
  • Comparison with the baseline: the syntactic and overall fixedness measures substantially outperform PMI, even when PMI is computed over syntactic (verb–object) relations.
  • Canonical form detection: a pattern counts as a canonical form when the pair's z-score frequency in that pattern exceeds a threshold tuned on development data. The predicted forms were checked against the dictionaries OD-CIE and CCID on 100 idiomatic test pairs.
    • Average precision was 81.7% and average recall 88.0%.
    • 69 pairs had perfect precision and recall.
    • For most pairs, the top-scoring form appears in the dictionary entry.

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

Cited by

44 papers of our library cite it (of the 140673 whose bibliographies we read); 9 of them have a page here, 35 not yet.

Cite

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

@inproceedings{fazly2006automatically,
  title = {{Automatically Constructing a Lexicon of Verb Phrase Idiomatic Combinations}},
  author = {Fazly, Afsaneh and Stevenson, Suzanne},
  booktitle = {11th Conference of the European Chapter of the Association for Computational Linguistics},
  year = {2006},
  url = {https://aclanthology.org/E06-1043/}
}

References

28 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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