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Corpus Statistics Meet the Noun Compound: Some Empirical Results

What it is about

The paper applies the Hindle and Rooth idea of corpus association to noun compounds: which bracketing of a three-noun compound, left or right, is correct. Two models are compared: an adjacency model, which uses neighbouring nouns, and a dependency model, which uses the head-modifier structure. Several training schemes are tried, with word and concept associations. The test set has 244 compounds that received a parse, taken from an 8-million-word encyclopedia corpus, and the training data had those test compounds removed. The dependency model reaches 77.5% against 68.9% for adjacency, a difference the paper reports as significant at the 5% level for the pattern training scheme. The best tuned technique reaches 81%, against a 67% baseline. Left-branching compounds make up 67% of the test set.

Ideas

For Mova

The chapter is 18. Compounding, conversion and the minor processes, with 22. Phrases: noun, adjective, adverb and prepositional phrases for the bracketing. The code is en/src/parse.rs (the candidate choice) and onto/src/wsd.rs (concept back-off). The paper's data is an encyclopedia corpus, not Mova data; any figure must come from EWT test.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.3115/981658.981665

@inproceedings{lauer1995corpus,
  title = {{Corpus Statistics Meet the Noun Compound: Some Empirical Results}},
  author = {Lauer, Mark},
  booktitle = {33rd Annual Meeting of the Association for Computational Linguistics},
  year = {1995},
  doi = {10.3115/981658.981665},
  url = {https://doi.org/10.3115/981658.981665}
}

References

25 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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