The Notion of Argument in Prepositional Phrase Attachment
What it is about
The paper treats PP attachment as a four-way problem: verb or noun site, crossed with argument or adjunct. It learns the argument-adjunct distinction from Penn Treebank data, using features that are lexical items, verb and noun classes, and diagnostics taken from linguistic tests of argumenthood. The best feature set reaches 79.8% accuracy for verb-attached PPs, a 37% reduction of error over the baseline. In the four-way task, arguments have 80% precision and 85% recall, and adjuncts have 80% precision and 73% recall. Attachment-site accuracy is 83.6% without "of", and falls only to 82.7% in the four-way setting. For verb arguments, a support vector machine raises the F-score from 36.0% to 55.9%.
Ideas
- I1.
en/src/expert.rs: write the linguistic diagnostics as explicit rules (optionality, iterativity, dependence on the head, order) that label each PP as argument or adjunct and print the rule that fired. Measure accuracy on a hand-checked sample of UD EWT PPs, and the agreement of the rules with the labels they give. - I2.
world/src/frames.rsandonto: map the verb of each PP to its VerbNet class and the PP-internal noun to its WordNet lexicographer file, and feed both as features to theenperceptron inen/src/parse.rs. The paper finds classes more useful than single words; measure LAS and PP accuracy on EWT test. - I3. Keep the attachment site as step one and the argument label as step two, and report both. Measure how far the two-way attachment accuracy falls on EWT test, as the paper's 83.6% to 82.7% does.
For Mova
The chapters are 23. Complementation and 22-phrases.md. The code is en/src/expert.rs, en/src/parse.rs and world/src/frames.rs. The data is Penn Treebank, so the paper's tables are for test only; the rule set and the class features must be checked on UD EWT test before any claim.
Cite
@article{merlo2006notion,
title = {{The Notion of Argument in Prepositional Phrase Attachment}},
author = {Merlo, Paola and Ferrer, Eva Esteve},
journal = {Computational Linguistics},
year = {2006},
doi = {10.1162/coli.2006.32.3.341},
url = {https://doi.org/10.1162/coli.2006.32.3.341}
}References
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