The Role of Modifier and Head Properties in Predicting the Compositionality of English and German Noun-Noun Compounds: A Vector-Space Perspective

What it is about

The paper asks which properties of a noun-noun compound's modifier and head make its degree of compositionality easier to predict from a vector space. It uses co-occurrence vectors (sentence-internal window of 20 words each side, local mutual information) from the COW corpora and the cosine between compound and constituent. The new German gold standard, GhoSt-NN, has 180 compounds (balanced) and 868 compounds (extended), rated for compositionality on a 1–6 scale. For English, the compound-head prediction reaches Spearman ρ = 0.60 on the Reddy set (90 compounds), while the both-constituent score on the 1,443-item Ó Séaghdha set is only 0.35. Compounds with low-frequency, low-productivity and low-ambiguity heads are predicted better; the ambiguity effect is significant in the extended GhoSt-NN set (p < 0.01). Relation types also differ, with BE compounds predicted best and ACTOR compounds worst.

Ideas

• I1. en/src/dict.rs: the head's number of senses, from the lexicon, as a gate on the compositional reading. Heads with more than two senses get a "lexicalised" flag. Measure on English compounds with the standard ratings, if a licensed set can be used; control with a random flag.
• I2. world/src/dictionary.rs: the same ambiguity feature on pupil compounds ("birthday cake", "sunflower"). Check that the compound's sense is chosen from the correct head sense in the school questions.
• I3. Relation classes as rules in prag: the relation (for example "made of", "for", "about") from prepositional and verbal cues, with per-class accuracy reported. Keep the relation enum deterministic.
• I4. A count-based replacement for the vector cosine: pointwise mutual information over corpus counts in BTreeMap tables, run twice to confirm identical output.

For Mova

The English chapter is 18. Compounding, conversion and the minor processes, and the lexicon is en/src/dict.rs. The paper's results are correlations, not a rule, so Mova would need its own gold data. The German data are not for Mova now. The clearest English point is the head-ambiguity effect, which can be measured with the existing sense table.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/s16-2020

@inproceedings{walde2016role,
  title = {{The Role of Modifier and Head Properties in Predicting the Compositionality of English and German Noun-Noun Compounds: A Vector-Space Perspective}},
  author = {Walde, Sabine Schulte im and Hätty, Anna and Bott, Stefan},
  booktitle = {Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics},
  year = {2016},
  doi = {10.18653/v1/s16-2020},
  url = {https://doi.org/10.18653/v1/s16-2020}
}

References

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

1. Marco Baroni, Silvia Bernardini, Adriano Ferraresi, and Eros Zanchetta. 2009. The WaCky Wide Web: A Collection of Very Large Linguistically Processed Web-Crawled Corpora. Language Resources and Evaluation, 43(3):209–226. find
2. Marco Baroni, Raffaella Bernardi, and Roberto Zamparelli. 2014. Frege in Space: A Program for Compositional Distributional Semantics. Linguistic Issues in Language Technologies, 9(6):5–110. find

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