# Semantic properties of English nominal pluralization: Insights from word embeddings

arXiv: https://arxiv.org/abs/2203.15424 · HTML: https://mlsanity.com/paper/2203.15424/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 105 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Distributional Semantics and Linguistic Theory](https://mlsanity.com/paper/1905.01896/) (2019)
- [Clustering with t-SNE, provably](https://mlsanity.com/paper/1706.02582/) (2017)
- [An Analysis of the t-SNE Algorithm for Data Visualization](https://mlsanity.com/paper/1803.01768/) (2018)
- [Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation](https://mlsanity.com/paper/1906.10007/) (2019)
- [Evaluating Word Embedding Models: Methods and Experimental Results](https://mlsanity.com/paper/1901.09785/) (2019)
- [Issues in evaluating semantic spaces using word analogies](https://mlsanity.com/paper/1606.07736/) (2016)
- [Learning Zero-Shot Multifaceted Visually Grounded Word Embeddings via Multi-Task Training](https://mlsanity.com/paper/2104.07500/) (2021)
- [Modeling morphology with Linear Discriminative Learning: considerations and design choices](https://mlsanity.com/paper/2106.07936/) (2021)

## Built on by (most cited first, 2 of 2)

- [Is deeper always better? Replacing linear mappings with deep learning networks in the Discriminative Lexicon Model](https://mlsanity.com/paper/2410.04259/) (2024)
- [Making sense of spoken plurals](https://mlsanity.com/paper/2207.01947/) (2022)

## Abstract (arXiv)

Semantic differentiation of nominal pluralization is grammaticalized in many languages. For example, plural markers may only be relevant for human nouns. English does not appear to make such distinctions. Using distributional semantics, we show that English nominal pluralization exhibits semantic clusters. For instance, pluralization of fruit words is more similar to one another and less similar to pluralization of other semantic classes. Therefore, reduction of the meaning shift in plural formation to the addition of an abstract plural meaning is too simplistic. A semantically informed method, called CosClassAvg, is introduced that outperforms pluralization methods in distributional semantics which assume plural formation amounts to the addition of a fixed plural vector. In comparison with our approach, a method from compositional distributional semantics, called FRACSS, predicted plural vectors that were more similar to the corpus-extracted plural vectors in terms of direction but not vector length. A modeling study reveals that the observed difference between the two predicted semantic spaces by CosClassAvg and FRACSS carries over to how well a computational model of the listener can understand previously unencountered plural forms. Mappings from word forms, represented with triphone vectors, to predicted semantic vectors are more productive when CosClassAvg-generated semantic vectors are employed as gold standard vectors instead of FRACSS-generated vectors.