Semantic properties of English nominal pluralization: Insights from word embeddings

What it is about

The standard view treats the English plural as the addition of one abstract meaning ("more than one") to a singular. The authors test this with word2vec vectors on 14,699 singular-plural noun pairs from subtitles of US television news (NewsScape). The shift from singular to plural vectors clusters by WordNet supersense: animal and person plurals shift in different directions, and the average shift vector sits close to the origin, so it "is blind to this rich structure". Using 411 finer semantic classes, a linear discriminant classifier assigns shift vectors to their class with 61% accuracy, while predicting the class from the singular vector alone reaches a weighted F-score of only 32% on the test folds (about 100 times the most-frequent baseline). The class-specific model, CosClassAvg, adds a class-average shift to the singular vector. It puts the true plural among the top 10 neighbours for 93% of nouns, against 91% for the general shift (3CosAvg) and 88% for the singular alone. A matrix model (FRACSS) gets closer in angle, but in a word-comprehension simulation its test-set errors are 726 against 501 for CosClassAvg, out of 1,034 test tokens.

Ideas

• I1. 11. Nouns: plural formation by semantic class as a table of counts, not a vector. For each class (fruit, animal, person, vehicle) count which plural ending and which exceptions occur in a lexicon, then predict the ending for an unseen noun by class majority, falling back to -s. Report the accuracy by class against the default -s rule.
• I2. en/src/morph.rs: a test split by seen and unseen stems, as in the paper's comprehension study. The rule must be measured on the unseen-stem plurals, because seen stems can be memorised.
• I3. Honest evaluation: when a plural is predicted, allow the singular as a candidate and report how often it wins. The paper shows the singular is the nearest neighbour for the general shift. Mova's own plural rule should report the same count.
• I4. 33. Implicature and presupposition: "bring your children" is number-neutral and "bring your child" is blocked when more than one child is possible. Encode this as a rule in prag (quantity implicature) and test it on the paper's two sentences plus a negative control with a numeral.

For Mova

The chapters are 11. Nouns (number, count and mass nouns) and 33. Implicature and presupposition (the plural implicature). The code points are en/src/morph.rs (plural rules and the class table) and world/src/dictionary.rs (noun senses that carry a class). The measure: plural-form accuracy by class on unseen nouns, against the -s default, with the singular-as-candidate count reported, run twice for determinism. The paper's vector models are not for Mova; the class counts are.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. arXiv 2203.15424

@misc{shafaeibajestan2022semantic,
  title = {{Semantic properties of English nominal pluralization: Insights from word embeddings}},
  author = {Shafaei-Bajestan, Elnaz and Moradipour-Tari, Masoumeh and Uhrig, Peter and Baayen, R. Harald},
  year = {2022},
  eprint = {2203.15424},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2203.15424}
}

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