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Automatic induction of FrameNet lexical units

Summary

FrameNet has limited coverage, with only about 10,000 lexical units (LUs). This has repeatedly undermined its use in QA and RTE systems. The authors address LU induction: automatically assigning words absent from FrameNet to the correct frame(s) among roughly 800 candidates.

They propose two unsupervised models and a hybrid:

  • Distributional model: represents each frame as a frequency-weighted centroid of its LUs' co-occurrence vectors and ranks frames by cosine similarity. It is tested with word-window, syntax-based and mixed (dependency-path) spaces.
  • WordNet-based model: builds per-frame hyponymy sub-graphs for nouns, verbs and adjectives, scoring nouns with conceptual density.
  • Combined model: merges the two.

Evaluation used leave-one-out on the FrameNet gold standard. The WordNet approach is more precise but covers fewer words. Distributional spaces cover nearly any word and do not require WordNet, so they suit low-resource languages. The combination gives the best balance between accuracy and coverage, beating earlier systems. Simple word-based spaces outperformed richer syntactic ones. The authors argue the results are good enough to support semi-automatic extension of FrameNet and bootstrapping of FrameNets in new languages.

Key points

  • Resource scale: FrameNet has 795 frames, about 10,000 LUs and 135,000 annotated BNC sentences, compared with roughly 210,000 entries in WordNet 3.0.
  • Comparison with Detour (Burchardt et al., 2005): Detour reports 0.38 best-1 accuracy at 87% coverage. The combined model surpasses it by about 5% in accuracy and 8% in coverage, and also supports best-k ranking.
  • Comparison with Johansson & Nugues (2007), an SVM over WordNet features: at 80% coverage their system reaches about 0.42 accuracy, roughly 10 points below the best WordNet-based model here. At 90% coverage it falls below 0.10.
  • Frequency threshold: restricting the distributional model to LUs with at least 200 corpus occurrences raises accuracy by 0.12 but lowers coverage to 57%.
  • SemEval-2007 test: on 24 unknown LUs, WNet-full scores 0.25 (best-1) and 0.69 (best-10) at 67% coverage. Many apparent errors are plausible frames, so reported accuracies should be read as lower bounds.

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

Topics: Semantic role labeling

Cited by

26 papers of our library cite it (of the 140673 whose bibliographies we read); 9 of them have a page here, 17 not yet.

Cite

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

@inproceedings{pennacchiotti2008automatic,
  title = {{Automatic induction of FrameNet lexical units}},
  author = {Pennacchiotti, Marco and Cao, Diego De and Basili, Roberto and Croce, Danilo and Roth, Michael},
  booktitle = {Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing},
  year = {2008},
  doi = {10.3115/1613715.1613773},
  url = {https://doi.org/10.3115/1613715.1613773}
}

References

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