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The Proposition Bank: An Annotated Corpus of Semantic Roles

What it is about

PropBank adds predicate-argument labels to the Penn Treebank (Wall Street Journal part): numbered roles Arg0 to Arg5 that are defined per verb sense, plus adjunct tags (ArgM with function types such as temporal, locative, manner). Each verb sense has a frameset in a frames file. The WSJ part covers over 3,300 verbs and just over 4,500 framesets; 2,581 of the 3,342 verbs have only one frameset, and inter-annotator agreement on frameset choice is 94%. A rule-based tagger reached 83% accuracy on pilot data, and annotators corrected its output by hand. A statistical role labeller trained on a preliminary release (72,109 predicate-argument structures) reached 82.8% role accuracy with gold parses and 80.9% with automatic parses, after dropping predicates with fewer than 10 training examples. Adding the treebank's trace information raised performance by roughly 5%.

Ideas

For Mova

The chapters are 13. Verbs, 28. Voice and the passive and 21. The simple clause. The code is prag/src/roles.rs and prag/src/frames.rs, with the lexicon in en/src/dict.rs. Measure role accuracy on the school test sentences, split by frameset count, and run it twice to confirm identical output.

Topics: Treebanks · Semantic role labeling

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.1162/0891201053630264

@article{palmer2005proposition,
  title = {{The Proposition Bank: An Annotated Corpus of Semantic Roles}},
  author = {Palmer, Martha and Gildea, Daniel and Kingsbury, Paul},
  journal = {Computational Linguistics},
  year = {2005},
  doi = {10.1162/0891201053630264},
  url = {https://doi.org/10.1162/0891201053630264}
}

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