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Classification of Verb-Particle Constructions with the Google Web1T Corpus

What it is about

The paper builds a type-level classifier for verb-particle constructions (VPCs), with no parser and no sentence context. For each verb-particle pair it runs the fronting test: it compares the Web1T n-gram counts of the two orders, "verb particle NP" and "verb NP particle", and decides by a learned rule. The evaluation uses 2,000 true VPCs and 2,000 non-VPC pairs. The classifier reaches an F-score of 75.7%, with accuracy 74.7%, recall 74.7% and precision 76.8%, and it handles thousands of queries a second. The authors say that a parser-based method reaches 97.4% F, but only on frequent VPCs in context, so the lightweight method trades accuracy for coverage.

Ideas

For Mova

The chapter is 23. Complementation, with 13. Verbs for the particle verbs. The code is en/src/lexgen.rs (the pair list) and en/src/parse.rs (the feature). Web1T is not in Mova; build the counts from the Mova collection. The 2,000-pair sets come from NC resources, so they are for tests only.

Cite

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

@inproceedings{kummerfeld2008classification,
  title = {{Classification of Verb-Particle Constructions with the Google Web1T Corpus}},
  author = {Kummerfeld, Jonathan K. and Curran, James R.},
  booktitle = {Proceedings of the Australasian Language Technology Association Workshop 2008},
  year = {2008},
  url = {https://aclanthology.org/U08-1008/}
}

References

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