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Verb Class Disambiguation Using Informative Priors

Paper · 2004 · Mirella Lapata and Chris Brew

Summary

Levin's (1993) verb classes group English verbs by shared diathesis alternations, but the inventory only lists which classes a verb can belong to. It gives no indication of which reading is more likely. This matters because many frequent verbs have several classes, and the frame alone often cannot settle the reading. For example, write in a double-object frame may be Message Transfer or Performance. Lapata and Brew turn Levin's inventory into a simple probabilistic model of class ambiguity. Its parameters are estimated from the British National Corpus (BNC) without any sense-annotated data. The model yields class preferences for ambiguous verbs. These preferences then serve as informative priors in a verb sense disambiguator, which also uses contextual cues standing in for world knowledge. According to the abstract, the priors help disambiguation. The provided excerpt omits the experimental sections, so exact scores are not reported here. The work matters because it adds likelihood information to a widely used lexical-semantic resource cheaply, with no manual annotation.

Key points

  • Levin indexes 3,024 verbs in roughly 200 classes. Of these verbs, 784 belong to more than one class.
  • Monosemous verbs outnumber polysemous ones about 4:1, yet their BNC frequencies are comparable: 4,252,715 vs. 3,986,014. As a result, close to half of verb tokens a semantic tagger sees are ambiguous.
  • Ambiguity rises with the number of alternations a verb licenses:
    • Among one-alternation verbs, 90.4% have a single class.
    • Among six-alternation verbs, only 14% have a single class, and 53.6% have four.
  • Syntactic frames sometimes resolve the class on their own. Serve, for instance, belongs to Give, Fit, Masquerade and Fulfilling, each tied to distinct frames. Other cases need priors plus context: write, toast, study and convey.
  • The class preferences are learned without a disambiguated corpus and are used as priors for verb sense disambiguation, with Levin classes treated as the sense inventory.

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

 Institutions

🎓University of SheffieldSheffield · United Kingdom117 works · 22,645 citations🎓The Ohio State UniversityColumbus · United States90 works · 19,112 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksPsychology · 1,649 worksExperimental and Cognitive Psychology · 930 works

Cite

Cite as Mirella Lapata and Chris Brew. 2004. Verb Class Disambiguation Using Informative Priors. Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/089120104773633385

@article{lapata2004verb,
  title = {{Verb Class Disambiguation Using Informative Priors}},
  author = {Mirella Lapata and Chris Brew},
  journal = {Computational Linguistics},
  year = {2004},
  doi = {10.1162/089120104773633385},
  url = {https://doi.org/10.1162/089120104773633385}
}

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