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Semantic Role Labeling of Implicit Arguments for Nominal Predicates

Paper · 2012 · Matthew Gerber and Joyce Y. Chai

Summary

Standard semantic role labeling (SRL) for nouns searches for a predicate's arguments only within its own clause or sentence. However, many nominal predicates have participants that are not stated locally but can be found elsewhere in the discourse. Gerber and Chai study these implicit arguments for noun predicates from NomBank, using Penn TreeBank documents. They manually annotated implicit arguments for ten NomBank predicates and found that these arguments expand the existing NomBank argument structures by 71%.

Using this corpus, they trained a discriminative classifier that looks for fillers beyond sentence boundaries. It draws on a broad feature set covering:

  • syntactic context
  • coreference probabilities
  • WordNet- and VerbNet-based semantic similarity
  • discourse relations
  • knowledge about typical event sequences extracted from large corpora

The model reaches an F1 of about 50%, well above an informed baseline. Recovering unstated participants is essential for interpreting connected text coherently. It can also benefit downstream applications such as question answering, information extraction and machine translation.

Key points

  • New resource: hand-annotated implicit arguments for ten NomBank nominal predicates. Any argument missing from NomBank's annotation counts as implicit, so the candidate search extends across sentences. An example is SEC filling the unannotated arg 0 of "requirement" and of "change" in a later sentence.
  • Coverage gain: adding implicit arguments increases NomBank's argument structures by 71%.
  • Model: LibLinear classifier with per-fold feature selection over roughly 80 candidate features. Examples include coreference probability, VerbNet path length, Wu & Palmer WordNet similarity, TF cosine similarity of role descriptions, and discourse relations.
  • Results: in ten-fold evaluation, per-fold F1 ranged from 45.8% to 55.8% (about 50% overall). Baseline F1 was roughly 23–35%, and the oracle upper bound was roughly 85–91%.
  • Motivation: discourse-level role recovery answers questions that sentence-level SRL cannot. An example is identifying who changed the rules in a two-sentence news passage.

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 VirginiaCharlottesville · United States36 works · 9,730 citations🎓Michigan State UniversityEast Lansing · United States23 works · 1,077 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.Semantic role labeling · 145 works

Cite

Cite as Matthew Gerber and Joyce Y. Chai. 2012. Semantic Role Labeling of Implicit Arguments for Nominal Predicates. Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00110

@article{gerber2012semantic,
  title = {{Semantic Role Labeling of Implicit Arguments for Nominal Predicates}},
  author = {Matthew Gerber and Joyce Y. Chai},
  journal = {Computational Linguistics},
  year = {2012},
  doi = {10.1162/coli\_a\_00110},
  url = {https://doi.org/10.1162/coli_a_00110}
}

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