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A Method for Automatically Constructing Case Frames for English

Paper · 2008 · Daisuke Kawahara and Kiyotaka Uchimoto

Summary

The paper builds a large lexicon of semantics-oriented case frames for English verbs. Each frame records which argument slots a verb takes and which nouns typically fill them, with frequencies. Subcategorization lexicons capture only syntactic patterns, so they lump together different senses of a verb. The authors instead derive frames from automatic parses of a raw corpus.

From the English Gigaword, they keep only short sentences, tag them, and parse them with MSTParser. They then take predicate-argument structures only from sentences that have a single active main verb. These structures are grouped by a dominant argument (object first, then subject, then a prepositional phrase). The resulting groups are merged by clustering that weighs both slot-pattern overlap and WordNet-based word similarity.

The output covers about 9,300 verbs, with 5.5 frames per verb on average. A manual check of 20 random verbs found 88.4% of frames correct.

The resource matters because it gives selectional preferences at the level of observed words, which parsers, translation and paraphrasing systems can use directly.

Key points

  • Source data: English Gigaword Second Edition (LDC2005T12), about 100M newswire sentences. Restricting to sentences of 20 words or fewer left roughly 47M.
  • Pipeline:
    • Tsuruoka's tagger for tagging.
    • MSTParser trained on WSJ sections 2–21 of the Penn Treebank.
    • On section 23, the parser reaches 89.9% labeled dependency accuracy and 36.3% complete sentences.
    • On short test sentences, these rise to 91.5% and 56.4%.
  • Slots are surface cases (sbj, obj, obj2, pp:in, pp:at, …). Fillers are lemmatized, lowercased headwords with frequencies, not semantic classes.
  • Clustering uses the similarity measure of Kawahara and Kurohashi (2006), with word similarity computed by WordNet::Similarity.
  • Result: about 9,300 verb entries at 5.5 frames per verb, with 88.4% accuracy on 20 hand-checked verbs.
    • The main errors came from choosing the wrong dominant argument.
    • Some errors came from clustering, which did not treat WordNet synsets.

Interesting ideas

  • I1. Trading recall for precision by harvesting only from inputs where the parser is reliable (short sentences, one active main verb) means corpus size compensates for discarded data. This filtering approach suits any knowledge mined from noisy automatic analyses.
  • I2. Seeding sense distinctions by a fixed priority of dominant slot (object > subject > PP) is a cheap, explainable first cut at verb sense splitting: "borrow money" and "borrow an idea" land in separate frames before any clustering happens.
  • I3. Using raw filler words with counts rather than a fixed semantic class inventory sidesteps the problem of picking a generalization level. Generalization is deferred to similarity at use time.
  • I4. Merging clusters by combining slot-pattern similarity with lexical similarity keeps frames consistent in both syntax and meaning, rather than in one dimension alone.
  • I5. The evaluation applies explicit per-frame criteria: no mixed senses, obligatory slots present, and stray fillers tolerated only in non-dominant slots. This makes manual judgments of an induced lexicon repeatable.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): Silver data agreed by witness parsers could be further restricted to short single-clause sentences before verb knowledge is extracted, keeping the induced lexicon clean.
  • I2 (frames): Frame selection in prag could key candidate senses on the head of the object slot, giving a traceable reason why one reading was chosen over another.
  • I3 (world): Compiled verb knowledge could store frequency-weighted observed fillers per slot, with WordNet similarity applied on demand to judge whether a new entity plausibly fills a role.
  • I4 (frames): When aligning parsed clauses with VerbNet frames, scoring both argument-pattern match and filler similarity could reduce metaphor-driven misassignments.
  • I5 (learn): Similar checklists could serve as a rubric for LLM teacher tiers grading self-induced frames, treating a contaminated dominant slot as a hard failure.

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

🔬National Institute of Information and Communications TechnologyTokyo · Japan23 works · 2,090 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.English · 2,263 works

Cite

Cite as Daisuke Kawahara and Kiyotaka Uchimoto. 2008. A Method for Automatically Constructing Case Frames for English. Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08).

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

@inproceedings{kawahara2008method,
  title = {{A Method for Automatically Constructing Case Frames for English}},
  author = {Daisuke Kawahara and Kiyotaka Uchimoto},
  booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
  year = {2008},
  doi = {10.63317/4q5b9fuga22p},
  url = {https://doi.org/10.63317/4q5b9fuga22p}
}

References

23 references, 8 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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